A comprehensive review of datasets and deep learning techniques for vision in Unmanned Surface Vehicles

Ali Anwar; Linh Trinh; Siegfried Mercelis

REVIEW 4 major objections 5 minor 216 references

A comprehensive review of datasets and deep learning techniques for vision in Unmanned Surface Vehicles

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A comprehensive review of 38 public USV vision datasets and the deep learning techniques trained on them finds that missing 3D data, calibration, annotations, metadata, and privacy protection—not model design—are what hold the field back.

desk verdict A useful survey of USV vision datasets and methods whose central gap analysis is undercut by internal inconsistencies in the dataset inventory, especially the calibration claim. read the letter →

arxiv 2412.01461 v1 pith:CXQBKTAU submitted 2024-12-02 cs.CV

Linh Trinh , Siegfried Mercelis , Ali Anwar This is my paper · ORCID

classification cs.CV

keywords unmannedsurfacevehiclesvisiondatasetsdeeplearningmaritimeperceptionobjectdetectionsegmentationmulti-sensorfusionsurvey

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Unmanned surface vehicles (USVs) need vision to navigate, but this review argues that progress is currently blocked less by the algorithms than by the data available to train them. The authors compile and analyze 38 public datasets collected in real-world maritime conditions, covering object detection, segmentation, classification, tracking, and raw multi-sensor recordings. They report that no public USV dataset supports 3D perception tasks such as 3D detection, 3D segmentation, or depth estimation, and that calibration data, consistent object annotations, and privacy safeguards are largely absent. If the review's inventory is correct, then the field's next advances will come from building shared, well-annotated, privacy-aware maritime datasets rather than from novel network architectures.

What carries the argument

The carrying mechanism is the dataset inventory itself: a structured catalog of 38 datasets (Tables 2–6) with uniform attributes including sensor type, resolution, FPS, tasks, number of annotated object classes, availability, field of view, and metadata. The work it does is to make gap analysis possible: by aligning all datasets on the same attribute axes, the review turns scattered release papers into a countable evidence base that supports negative claims such as 'no public 3D dataset' and comparative claims such as 'USV datasets trail AV datasets in every sensor and task category.' A second mechanism is the taxonomy of deep learning techniques, split into single-sensor and multi-modal branches, which shows that most USV-specific work reuses off-the-shelf architectures (YOLO, UNet, DeepLab, Mask R-CNN, transformers) and that innovation is concentrated in small modifications rather than fundamental building blocks.

What would settle it

A reader could check release listings and preprint archives up to mid-2024: finding a public USV dataset that provides 3D object detection or depth ground truth with sensor calibration would directly refute the central claim, as would showing that any of the 38 listed 'not open' or 'not public' datasets is in fact openly downloadable and changes the counts on which the conclusions rest.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is a gap analysis: compared both with earlier maritime surveys and with the much larger autonomous-vehicle (AV) dataset ecosystem, USV vision is starved of exactly the data that modern perception systems depend on. The authors count 38 public datasets collected in real-world USV scenarios and categorize them by sensors, tasks, resolution, frame rate, number of annotated object classes, availability, field of view, area, location, and metadata. Their analysis finds zero public datasets supporting 3D object detection, zero supporting 3D segmentation, and zero supporting depth estimation, while 2D object detection has 15 public datasets and AV counterparts have 55. Among the larger multi-sensor collections (LiDAR, radar, stereo, sonar), the authors find that calibration data and annotations on those sensors are almost always missing, and that most multi-modal fusion methods reported in the literature are therefore validated on private, self-collected data. The authors further document sparse and inconsistent object annotations across datasets, minimal metadata describing weather, lighting, or water conditions, and a privacy deficit in which only one dataset claims to blur human identities and another visibly leaks a face. The conclusion the authors draw is that the central bottleneck for USV vision is data infrastructure, not model design.

Load-bearing premise

The paper's gap analysis stands or falls on the completeness and accuracy of its list of 38 public USV vision datasets; if a public dataset that supports 3D perception or supplies calibration was missed, the headline claims about missing data would be wrong.

Editorial extensions

If this is right

Researchers and operators should assume that any USV perception system needing 3D information, such as depth or 3D bounding boxes, must generate its own annotations or adapt models from AV data, because no public maritime dataset currently provides them.
Multi-sensor fusion work (camera-LiDAR, camera-radar, camera-LiDAR-radar) will stay confined to private datasets until public releases include synchronized annotations and calibration matrices, which the paper shows are almost never supplied.
Comparable benchmarking across USV datasets is impossible until object-class definitions are standardized; the paper shows very little overlap in annotated objects, which blocks transfer learning and domain adaptation.
Privacy-aware data release becomes a prerequisite for future datasets: at least one widely used public dataset contains a recognizable human face, and no dataset explains its de-identification procedure, exposing the field to regulatory risk.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the review's exclusion of synthetic data makes its 'no 3D dataset' claim narrower than it sounds; the authors' own technique review shows depth-estimation work already falls back on synthetic depth because real ground truth is absent, so simulated maritime 3D data may be the fastest bridge to 3D perception for USVs.
Editorial inference: a concrete test of the paper's bottleneck thesis is the pace of fusion research: if a public multi-sensor dataset with calibration appeared, the number of camera-LiDAR-radar fusion papers validated on public data should rise sharply, since the paper's own survey shows such methods are currently limited mainly by data, not by available architectures.
Editorial inference: the privacy leak the paper points to suggests that future dataset design should integrate de-identification at capture time (e.g., camera placement and resolution choices) rather than as a post hoc blur, because blurring removes the very fine detail detection and segmentation models need.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's the short version: this survey is genuinely useful as a map of 38 USV vision datasets and the deep learning methods applied to them. It fills a real gap, and the qualitative observation that the field is limited by data infrastructure—sparse annotations, missing metadata, no public 3D benchmarks, privacy leaks—will ring true to anyone who works in the area. The AV comparison and the annotated-object inventory are nice additions. But the paper's own inventory contradicts its headline claims, and those contradictions need to be fixed before I'd trust it as a reference.

The load-bearing problem is calibration. Section 2.1.2 says public USV datasets lack calibration data, and Section 4.1 repeats that 'almost no calibration data' exists. But Section 2.1.4 and Table 4 list ROAM CRAS and Pohang Canal as publicly accessible with calibration data. That's not a minor typo; it's the central negative claim of the survey. If two public datasets provide calibration, the argument that multimodal fusion is blocked by missing calibration needs to be re-worded and re-verified.

The same issue hits the dataset count. Section 2 excludes synthetic datasets, but Table 3 includes FoggyShipInsseg with sensor type 'Synthetic'. The prose also mentions Small ShipInsSeg, which doesn't appear in any table or in the 38 count. Year mismatches for Kolomverse (2022 in Table 2, 2024 in the citation) and SeaSAW (2023 vs 2022) suggest the tables weren't carefully checked. There's also no search protocol or inclusion criteria, so we can't independently audit the completeness of the 38-dataset inventory.

None of this sinks the core contribution. The survey consolidates a scattered literature, the taxonomy of techniques is reasonable, and the qualitative gap analysis is credible. But a survey whose findings are absence claims—'no public 3D data', 'no calibration'—has to be internally consistent with its own tables. Right now it isn't.

Who's this for? Anyone starting in USV perception who wants a quick map of datasets and methods. It deserves a serious referee, but it needs a careful revision pass before publication. I'd send it back for major revision, with instructions to fix the calibration contradiction, resolve the synthetic-dataset inclusion, document the search protocol, and reconcile all dataset counts.

Referee Report

4 major / 5 minor

Summary. This paper surveys vision datasets and deep learning techniques for Unmanned Surface Vehicles (USVs). It compiles 38 public datasets collected in real-world scenarios from 2015 to 2024, organizes them by task (object detection, segmentation, and other vision tasks), compares USV datasets with autonomous vehicle (AV) datasets, reviews single-sensor and multi-sensor deep learning methods, and concludes with a set of challenges and future directions, including the lack of public 3D perception datasets, sparse and inconsistent annotations, missing metadata, and privacy leaks.

Significance. If the inventory is accurate, this is a useful reference and gap analysis for the USV vision community. The paper's strengths include its broad coverage, a detailed chronological overview, a structured taxonomy of deep learning techniques, a quantitative comparison with AV datasets, and an explicit discussion of data privacy. However, the central contribution is the dataset inventory and the absence claims built on it, and those claims currently rest on an internally inconsistent set of tables and prose. The negative claims about calibration data and 3D perception benchmarks are plausible but need to be re-verified against the paper's own inventory before the survey can be relied upon.

major comments (4)

[Section 2, Table 3] The Section 2 introduction states that the authors 'disregard all synthetic datasets that are generated through simulation or deep learning generative models,' yet Table 3 lists FoggyShipInsseg (Sun et al., 2022b) with sensor type 'Synthetic' and includes it in the analysis. Since the paper's headline count of 38 real-world datasets depends on excluding synthetic data, this inconsistency directly affects the paper's central quantitative claim. Please either remove or reclassify FoggyShipInsseg, or revise the stated inclusion criterion to match the actual inventory.
[Sections 2.1.2, 2.1.4, 4.1, Table 4] The calibration gap is stated as a key finding: Section 2.1.2 says 'it lack of calibration data in the public dataset of USVs' and Section 4.1 says 'there was almost no calibration data supplied' for public multi-modal datasets. However, Section 2.1.4 explicitly states that ROAM CRAS (Campos et al., 2022) and Pohang Canal (Chung et al., 2023) 'also provided calibration data' and are publicly accessible, and Table 4 marks ROAM CRAS as Open/Limited and Pohang Canal as Open/Y while listing Calibration under their sensors. This internal contradiction undermines a load-bearing conclusion. The authors need to reconcile the prose with Table 4 and either strengthen or qualify the calibration-scarcity claim.
[Section 2.1.4, Tables 2-4] The claimed count of 38 datasets is not verifiable from the manuscript itself. Section 2.1.4 discusses a 'Small ShipInsseg' dataset (Sun et al., 2023b) that does not appear in any table and is not counted in the 38, and the same paragraph cites 'Campos (Jeong et al., 2024)' in a way that conflates two distinct datasets (ROAM CRAS and Catabot). Since the survey's contribution is precisely the completeness and accuracy of its inventory, the authors should provide a transparent enumeration of all included datasets, reconcile prose with tables, and correct the mislabeled citation.
[Section 2, Tables 2-4] The paper does not document its search protocol, inclusion/exclusion criteria, or the verification process for table attributes. This matters because the headline findings are absence claims: no public 3D perception datasets, almost no calibration data, and sparse metadata. Absence claims cannot be assessed without knowing the search scope, databases queried, keywords, date of search, and how each dataset entry was checked against the primary source. Additionally, there are unresolved date conflicts in the inventory: Kolomverse is cited as Nanda et al. (2024) but Table 2 lists year 2022, and SeaSAW is cited as Kaur et al. (2022) but Table 2 lists year 2023. Please add a methodology subsection and correct the year mismatches.

minor comments (5)

[Table 1] The table header contains a typo, 'detction', which should read 'detection'.
[Table 3] The caption describes 'panotpic segmentation,' which should be 'panoptic segmentation.'
[Figure 3] Dataset names are inconsistently capitalized, for example 'MariShipInsSeg' in Section 2.1.2 versus 'MariShipInsseg' in Figure 3; please standardize all dataset names.
[Table 2] The year column for Kolomverse (2022) conflicts with the reference 'Nanda et al., 2024' cited in the same row, and SeaSAW shows 2023 while the reference is 'Kaur et al., 2022'. These should be checked against the original publications.
[Section 2.1.4] The sentence 'Campos (Jeong et al., 2024) focuses on multi-domain inspection and maintenance of USVs' is confusing because Campos is the first author of the ROAM CRAS paper, while Jeong et al. (2024) is the Catabot reference. Please disambiguate the citation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the survey's findings are external summaries of published datasets and methods, not self-referential derivations.

full rationale

This paper is a review, not a derivation. Its central claims—that 38 public USV vision datasets exist, that no public dataset supports 3D perception tasks, that calibration and metadata are often missing, and that annotation object sets are sparse and inconsistent—are gap claims built from the inventory in Tables 2–6 and from comparisons with AV datasets. None of these claims is defined in terms of another claim it is supposed to support, and none is produced by fitting, prediction, or a uniqueness theorem. The claim to be 'the first study to provide an in-depth review of both USV deep learning techniques as well as datasets' is a positioning statement against the prior surveys listed in Table 1, not a result derived from the authors' own prior work. The repeated citations to Trinh et al. (2023a,b, 2024a,b,c) are used as motivation for future research directions such as data selection, privacy preservation, domain generalization, and dynamic scene change detection; these are recommendations, not load-bearing evidence for the survey's dataset inventory or taxonomy. No ansatz is imported from the authors' earlier papers, and no uniqueness claim is used to force a choice. The manuscript does contain internal inconsistencies—for example, FoggyShipInsseg is listed with sensor type 'Synthetic' in Table 3 after Section 2 states that synthetic datasets are excluded, and Section 2.1.2 says public USV datasets lack calibration while Section 2.1.4 and Table 4 credit ROAM CRAS and Pohang Canal with calibration data. These are factual consistency problems that affect the reliability of the headline gap claims, but they are not circularity: the conclusions do not reduce to their own inputs by construction. Accordingly, no circular step is identified.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

A review paper introduces no free parameters and no invented entities. The load-bearing assumptions are about the completeness and accuracy of the compiled dataset inventory, the scope exclusions (synthetic and non-USV-collected data), and the reliability of the secondary AV comparison counts.

assumptions (3)

domain assumption The 38-dataset inventory in Tables 2-4 is complete and correctly attributed for the period 2015 to June 2024.
Section 2 and the trend and gap analyses in Sections 4.1 and 4.2 derive directly from this inventory. The paper provides no search protocol or verification method, and the tables contain internal inconsistencies such as FoggyShipInsseg marked as synthetic despite the stated exclusion of synthetic data, and year mismatches for Kolomverse and SeaSAW.
domain assumption Synthetic datasets can be excluded because the sim-to-real gap limits their applicability to USV vision.
Section 2 states this exclusion, and it shapes the dataset set. However, Table 3 includes FoggyShipInsseg with sensor type 'Synthetic', which directly contradicts the stated criterion.
domain assumption The AV-versus-USV comparison numbers in Figure 5, aggregated from cited surveys, are accurate and comparable.
Figure 5 aggregates counts from prior AV surveys such as Karangwa et al. 2023, Liu et al. 2024a, Ma et al. 2024b, and Qian et al. 2022. The counting rules are not given, so the comparison is only as reliable as those secondary sources.

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Cite this review

Pith. "Pith review of A comprehensive review of datasets and deep learning techniques for vision in Unmanned Surface Vehicles." pith.science (2026). https://pith.science/paper/CXQBKTAU

@misc{pith2026241201461,
  author       = {Pith},
  title        = {Pith review of: A comprehensive review of datasets and deep learning techniques for vision in Unmanned Surface Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CXQBKTAU}},
  note         = {Machine review of arXiv:2412.01461}
}

read the original abstract

Unmanned Surface Vehicles (USVs) have emerged as a major platform in maritime operations, capable of supporting a wide range of applications. USVs can help reduce labor costs, increase safety, save energy, and allow for difficult unmanned tasks in harsh maritime environments. With the rapid development of USVs, many vision tasks such as detection and segmentation become increasingly important. Datasets play an important role in encouraging and improving the research and development of reliable vision algorithms for USVs. In this regard, a large number of recent studies have focused on the release of vision datasets for USVs. Along with the development of datasets, a variety of deep learning techniques have also been studied, with a focus on USVs. However, there is a lack of a systematic review of recent studies in both datasets and vision techniques to provide a comprehensive picture of the current development of vision on USVs, including limitations and trends. In this study, we provide a comprehensive review of both USV datasets and deep learning techniques for vision tasks. Our review was conducted using a large number of vision datasets from USVs. We elaborate several challenges and potential opportunities for research and development in USV vision based on a thorough analysis of current datasets and deep learning techniques.

Figures

Figures reproduced from arXiv: 2412.01461 by the authors.

**Figure 1.** Some platforms and applications in maritime. There are four main applications in maritime (Kiefer et al., 2023), which are monitoring, surveillance, inspection and navigation. Several platforms are being developed to support these applications, including aerial drones (UAVs) (Su et al., 2023; Zhang et al., 2021a; Rekavandi et al., 2022; Er et al., 2023), satellite (Su et al., 2023; Zhang et al., 2021a; Kanjir et al.… view at source ↗

**Figure 2.** Architecture of a typical USVs. L. Trinh et al.: Preprint submitted to Elsevier Page 1 of 25 arXiv:2412.01461v1 [cs.CV] 2 Dec 2024 [PITH_FULL_IMAGE:figures/full_fig_p001_2.png] view at source ↗

**Figure 3.** Chronological overview of vision datasets for USVs. Unlike previous surveys, our study focuses on a wide range of vision tasks such as 2D or 3D object detection, segmentation, classification, depth estimation, and many others for USVs. Furthermore, we conduct a systematic review of various sensors for maritime USV vision, including RGBimage (camera, stereo camera, IR-camera), LiDAR, radar, sonar, and others. We pr… view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Statistic of number of dataset by some characteristics. various maritime environments. There are three main environments in maritime contexts: ocean, river, and lake. We also consider the harbor as a specialized area. We classify the implicit category as an unknown ma…

**Figure 5.** Figure 5: Comparison of number of datasets between AVs and USVs in term of vision tasks and equipped vision sensors. autonomous systems such as AVs and USVs to perceive environmental, prior to further processing by decision making systems such as planning and control. There exi…

**Figure 6.** Figure 6: The taxonomy of vision techniques in USVs. exist two primary approaches for object detection methodologies, which are single-stage based and multi-stage based techniques. These approaches are categorized according to the configuration of the model network architecture…

**Figure 7.** Figure 7: Number of papers in single sensor based techniques for USVs. (OD: object detection, Seg: segmentation) Semantic segmentation. Several SOTA semantic segmentation models in the COCO dataset are applied for training segmentation models on USVs datasets. These works inclu…

**Figure 8.** Figure 8: Number of papers in multimodal sensor fusion techniques for USVs. 3.2.1. Camera-camera The fusion of multiple camera inputs (camera-camera) in USVs can improve spatial coverage, depth perception, and resistance to visual occlusions, thereby improving obstacle detectio…

**Figure 9.** Figure 9: A sample in MaSTr1478 dataset (Žust and Kristan, 2022) without data privacy. order to enhance USV functionality, particularly in the realm of USV vision. 4.7. Data Privacy Maintaining privacy in AVs data constitutes a paramount concern to tackle (Trinh et al., 2023b,a)…

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Reference graph

Works this paper leans on

216 extracted references · 52 canonical work pages

[1]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...
[2]

, author Michaelides, M.P

author Aslam, S. , author Michaelides, M.P. , author Herodotou, H. , year 2020 . title Internet of ships: A survey on architectures, emerging applications, and challenges . journal IEEE Internet of Things journal volume 7 , pages 9714--9727 . :https://doi.org/10.1109/jiot.2020.2993411

arXiv 2020
[3]

, author Kanarachos, S

author Batsch, F. , author Kanarachos, S. , author Cheah, M. , author Ponticelli, R. , author Blundell, M. , year 2022 . title A taxonomy of validation strategies to ensure the safe operation of highly automated vehicles . journal Journal of Intelligent Transportation Systems volume 26 , pages 14--33 . :https://doi.org/10.1080/15472450.2020.1738231

arXiv 2022
[4]

, author Janssens, R

author Billast, M. , author Janssens, R. , author Vanneste, A. , author Vanneste, S. , author Vasseur, O. , author Anwar, A. , author Mets, K. , author De Schepper, T. , author Oramas, J. , author Latr \'e , S. , et al., year 2022 . title Object detection to enable autonomous vessels on european inland waterways , in: booktitle IECON 2022--48th Annual Con...

arXiv 2022
[5]

, author Iocchi, L

author Bloisi, D.D. , author Iocchi, L. , author Pennisi, A. , author Tombolini, L. , year 2015 . title Argos-venice boat classification , in: booktitle 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) , pp. pages 1--6 . :https://doi.org/10.1109/AVSS.2015.7301727

arXiv 2015
[6]

, author Xiaoyang, X

author Bo, L. , author Xiaoyang, X. , author Xingxing, W. , author Wenting, T. , year 2021 . title Ship detection and classification from optical remote sensing images: A survey . journal Chinese Journal of Aeronautics volume 34 , pages 145--163 . :https://doi.org/10.1016/j.cja.2020.09.022

work page doi:10.1016/j.cja.2020.09.022 2021
[7]

, author Kristan, M

author Bovcon, B. , author Kristan, M. , year 2021 . title Wasr—a water segmentation and refinement maritime obstacle detection network . journal IEEE Transactions on Cybernetics volume 52 , pages 12661--12674 . :https://doi.org/10.1109/tcyb.2021.3085856

arXiv 2021
[8]

, author Mandeljc, R

author Bovcon, B. , author Mandeljc, R. , author Perš, J. , author Kristan, M. , year 2018 . title Stereo obstacle detection for unmanned surface vehicles by imu-assisted semantic segmentation . journal Robotics and Autonomous Systems volume 104 , pages 1--13 . :https://doi.org/10.1016/j.robot.2018.02.017

work page doi:10.1016/j.robot.2018.02.017 2018

Show all 216 references

[9]

, author Muhovič, J

author Bovcon, B. , author Muhovič, J. , author Perš, J. , author Kristan, M. , year 2019 . title The mastr1325 dataset for training deep usv obstacle detection models , in: booktitle 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. pages 3...

2019
[10]

, author Muhovič, J

author Bovcon, B. , author Muhovič, J. , author Vranac, D. , author Mozetič, D. , author Perš, J. , author Kristan, M. , year 2022 . title Mods—a usv-oriented object detection and obstacle segmentation benchmark . journal IEEE Transactions on Intelligent Transportation Systems...

2022
[11]

, author Deng, W

author Cai, J. , author Deng, W. , author Guang, H. , author Wang, Y. , author Li, J. , author Ding, J. , year 2022 . title A survey on data-driven scenario generation for automated vehicle testing . journal Machines volume 10 . :https://doi.org/10.3390/machines10111101

2022 doi
[12]

, author Meng, H

author Cai, S. , author Meng, H. , author Wu, J. , year 2024 . title Fe-yolo: Yolo ship detection algorithm based on feature fusion and feature enhancement . journal Journal of Real-Time Image Processing volume 21 , pages 1--13

2024
[13]

title California consumer privacy act

author California , year 2018 . title California consumer privacy act . howpublished https://www.oag.ca.gov/sites/all/files/agweb/pdfs/privacy/oal-sub-final-text-of-regs.pdf

2018
[14]

, author Matos, A

author Campos, D.F. , author Matos, A. , author Pinto, A.M. , year 2022 . title Modular multi-domain aware autonomous surface vehicle for inspection . journal IEEE Access volume 10 , pages 113355--113375 . :https://doi.org/10.1109/access.2022.3217504

2022
[15]

, author Zhang, Y

author Chen, C.H. , author Zhang, Y. , author Guo, W. , author Pan, M. , author Lyu, L. , author Lin, C.Y. , year 2020 a. title Contour accentuation for transfer learning-based ship recognition method , in: booktitle Companion Proceedings of the Web Conference 2020 , pp. pages 61--62

2020
[16]

, author Zhu, Y

author Chen, L.C. , author Zhu, Y. , author Papandreou, G. , author Schroff, F. , author Adam, H. , year 2018 a. title Encoder-decoder with atrous separable convolution for semantic image segmentation , in: booktitle Computer Vision – ECCV 2018 , publisher Springer Internation...

2018 doi
[17]

, author Zhu, Y

author Chen, L.C. , author Zhu, Y. , author Papandreou, G. , author Schroff, F. , author Adam, H. , year 2018 b. title Encoder-decoder with atrous separable convolution for semantic image segmentation , in: booktitle Proceedings of the European conference on computer vision (E...

2018
[18]

, author Sun, J

author Chen, M. , author Sun, J. , author Aida, K. , author Takefusa, A. , year 2024 . title Weather-aware object detection method for maritime surveillance systems . journal Future Generation Computer Systems volume 151 , pages 111--123 . :https://doi.org/10.1016/j.future.2023.09.030

2024 doi
[19]

, author Xiao, C

author Chen, Q. , author Xiao, C. , author Wen, Y. , author Yuan, H. , author Huang, Y. , author Liu, Y. , author Zhan, W. , author Li, Q. , year 2023 . title Knowledge-driven semantic segmentation for waterway scene perception . journal IEEE Sensors Journal

2023
[20]

, author Liu, Y

author Chen, X. , author Liu, Y. , author Achuthan, K. , year 2021 a. title Wodis: Water obstacle detection network based on image segmentation for autonomous surface vehicles in maritime environments . journal IEEE Transactions on Instrumentation and Measurement volume 70 , p...

2021
[21]

, author Liu, Y

author Chen, X. , author Liu, Y. , author Achuthan, K. , year 2021 b. title Wodis: Water obstacle detection network based on image segmentation for autonomous surface vehicles in maritime environments . journal IEEE Transactions on Instrumentation and Measurement volume 70 , p...

2021
[22]

, author Yang, Y

author Chen, X. , author Yang, Y. , author Wang, S. , author Wu, H. , author Tang, J. , author Zhao, J. , author Wang, Z. , year 2020 b. title Ship type recognition via a coarse-to-fine cascaded convolution neural network . journal The Journal of Navigation volume 73 , pages 813--832

2020
[23]

, author Dong, D

author Chen, Z. , author Dong, D. , author Zhao, B. , author Zhang, W. , year 2022 . title Water segmentation with superior guidance and aligned fusion strategy for unmanned surface vehicles in maritime environment , in: booktitle 2022 IEEE International Conference on Unmanned...

2022
[25]

, author Misra, I

author Cheng, B. , author Misra, I. , author Schwing, A.G. , author Kirillov, A. , author Girdhar, R. , year 2022 . title Masked-attention mask transformer for universal image segmentation , in: booktitle Proceedings of the IEEE/CVF conference on computer vision and pattern re...

2022
[26]

, author Zhu, Y

author Cheng, S. , author Zhu, Y. , author Wu, S. , year 2023 . title Deep learning based efficient ship detection from drone-captured images for maritime surveillance . journal Ocean Engineering volume 285 , pages 115440 . :https://doi.org/10.1016/j.oceaneng.2023.115440

2023
[27]

, author Jiang , M

author Cheng , Y. , author Jiang , M. , author Zhu , J. , author Liu , Y. , year 2021 . title Are we ready for unmanned surface vehicles in inland waterways? the usvinland multisensor dataset and benchmark . journal IEEE Robotics and Automation Letters volume 6 , pages 3964--3...

2021
[28]

, author Xu, H

author Cheng, Y. , author Xu, H. , author Liu, Y. , year 2021 a. title Robust small object detection on the water surface through fusion of camera and millimeter wave radar , in: booktitle Proceedings of the IEEE/CVF international conference on computer vision , pp. pages 15263--15272

2021
[30]

title China cybersecurity law (csl)

author China , year 2016 . title China cybersecurity law (csl) . howpublished http://www.cac.gov.cn/2016-11/07/c_1119867116.htm

2016
[31]

, author Li, L

author Chu, X. , author Li, L. , author Zhang, B. , year 2024 . title Make repvgg greater again: A quantization-aware approach , in: booktitle Proceedings of the AAAI Conference on Artificial Intelligence , pp. pages 11624--11632

2024
[32]

, author Kim, J

author Chung, D. , author Kim, J. , author Lee, C. , author Kim, J. , year 2023 . title Pohang canal dataset: A multimodal maritime dataset for autonomous navigation in restricted waters . journal The International Journal of Robotics Research volume 42 , pages 1104--1114 . :h...

2023 doi
[33]

, author DeFilippo, M

author Clunie, T. , author DeFilippo, M. , author Sacarny, M. , author Robinette, P. , year 2021 . title Development of a perception system for an autonomous surface vehicle using monocular camera, lidar, and marine radar , in: booktitle 2021 IEEE International Conference on R...

2021
[34]

, author Jain, A

author Contreras, M. , author Jain, A. , author Bhatt, N.P. , author Banerjee, A. , author Hashemi, E. , year 2024 . title A survey on 3d object detection in real time for autonomous driving . journal Frontiers in Robotics and AI volume 11 . :https://doi.org/10.3389/frobt.2024.1212070

2024
[35]

, author Mafra, S.B

author Cruz, M.R. , author Mafra, S.B. , author Teixeira, E.H. , author Oliveira, D.M. , author Figueiredo, F.A. , year 2023 . title Evaluating computer vision architectures for ship classification: A comparative study , in: booktitle Brazilian symposium on telecommunications ...

2023
[36]

, author Sacarny, M

author DeFilippo, M. , author Sacarny, M. , author Robinette, P. , year 2021 . title Robowhaler: A robotic vessel for marine autonomy and dataset collection , in: booktitle OCEANS 2021: San Diego – Porto , pp. pages 1--7 . :https://doi.org/10.23919/OCEANS44145.2021.9705871

2021
[37]

, author Li, W

author Ding, J. , author Li, W. , author Pei, L. , author Yang, M. , author Ye, C. , author Yuan, B. , year 2023 . title Sw-yolox: An anchor-free detector based transformer for sea surface object detection . journal Expert Systems with Applications volume 217 , pages 119560

2023
[38]

, author Heller, D

author Douguet, R. , author Heller, D. , author Laurent, J. , year 2023 . title Multimodal perception for obstacle detection for flying boats-unmanned surface vehicle (usv) , in: booktitle OCEANS 2023-Limerick , pp. pages 1--8

2023
[39]

, author Castaing, P

author Edmonds, S. , author Castaing, P. , author van Ratingen, M. , author Gentilleau, M. , author Petit-Boulanger, C. , year 2023 . title Euro ncap rescue--tertiary safety assessment , in: booktitle 27th International Technical Conference on the Enhanced Safety of Vehicles (ESV)

2023
[40]

, author Zhang, Y

author Er, M.J. , author Zhang, Y. , author Chen, J. , author Gao, W. , year 2023 . title Ship detection with deep learning: a survey . journal Artificial Intelligence Review volume 56 , pages 11825--11865 . :https://doi.org/10.1007/s10462-023-10455-x

2023 doi
[41]

title General data protection regulation (gdpr)

author Europe , year 2018 . title General data protection regulation (gdpr) . howpublished https://gdpr-info.eu/

2018
[43]

, author Heikkonen, J

author Farahnakian, F. , author Heikkonen, J. , year 2020 . title Deep learning based multi-modal fusion architectures for maritime vessel detection . journal Remote Sensing volume 12 , pages 2509

2020
[45]

, author Moschoyiannis, S

author Finlinson, A. , author Moschoyiannis, S. , year 2022 . title Semantic segmentation for multi-contour estimation in maritime scenes , in: booktitle Proceedings of the 19th ACM SIGGRAPH european conference on visual media production , pp. pages 1--10

2022
[46]

, author Li, Y

author Fu, H. , author Li, Y. , author Wang, Y. , author Li, P. , year 2018 . title Maritime ship targets recognition with deep learning , in: booktitle 2018 37th Chinese Control Conference (CCC) , pp. pages 9297--9302

2018
[47]

, author Zhang, J

author Gao, J. , author Zhang, J. , author Liu, C. , author Li, X. , author Peng, Y. , year 2022 . title Camera-lidar cross-modality fusion water segmentation for unmanned surface vehicles . journal Journal of Marine Science and Engineering volume 10 , pages 744

2022
[48]

, author Yao, S

author Guan, R. , author Yao, S. , author Liu, L. , author Zhu, X. , author Man, K.L. , author Yue, Y. , author Smith, J. , author Lim, E.G. , author Yue, Y. , year 2024 . title Mask-vrdet: A robust riverway panoptic perception model based on dual graph fusion of vision and 4d...

2024
[49]

, author Yao, S

author Guan, R. , author Yao, S. , author Zhu, X. , author Man, K.L. , author Lim, E.G. , author Smith, J. , author Yue, Y. , author Yue, Y. , year 2023 a. title Achelous: A fast unified water-surface panoptic perception framework based on fusion of monocular camera and 4d mmw...

2023
[50]

, author Yao, S

author Guan, R. , author Yao, S. , author Zhu, X. , author Man, K.L. , author Yue, Y. , author Smith, J. , author Lim, E.G. , author Yue, Y. , year 2023 b. title Efficient-vrnet: An exquisite fusion network for riverway panoptic perception based on asymmetric fair fusion of vi...

2023 arXiv
[51]

, author Solmaz, B

author Gundogdu, E. , author Solmaz, B. , author Y \"u cesoy, V. , author Koc, A. , year 2017 . title Marvel: A large-scale image dataset for maritime vessels , in: booktitle Computer Vision--ACCV 2016 , pp. pages 165--180 . :https://doi.org/10.1007/978-3-319-54193-8_11

2017 doi
[53]

, author Lovell, B.C

author Guo, K. , author Lovell, B.C. , year 2024 . title Domain-aware triplet loss in domain generalization . journal Computer Vision and Image Understanding volume 243 , pages 103979 . :https://doi.org/10.1016/j.cviu.2024.103979

2024
[54]

, author Farahnakian, F

author Haghbayan, M.H. , author Farahnakian, F. , author Poikonen, J. , author Laurinen, M. , author Nevalainen, P. , author Plosila, J. , author Heikkonen, J. , year 2018 . title An efficient multi-sensor fusion approach for object detection in maritime environments , in: boo...

2018
[55]

, author Wang, Y

author Han, K. , author Wang, Y. , author Tian, Q. , author Guo, J. , author Xu, C. , author Xu, C. , year 2020 . title Ghostnet: More features from cheap operations , in: booktitle Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. pages 1...

2020
[56]

, author Zhao, B

author Han, W. , author Zhao, B. , author Luo, J. , year 2023 . title Towards smaller and stronger: An edge-aware lightweight segmentation approach for unmanned surface vehicles in water scenarios . journal Sensors volume 23 , pages 4789

2023
[57]

, author Yao, L

author Hansen, K.F. , author Yao, L. , author Ren, K. , author Wang, S. , author Liu, W. , author Liu, Y. , year 2023 . title Image segmentation in marine environments using convolutional lstm for temporal context . journal Applied Ocean Research volume 139 , pages 103709

2023
[58]

, author Zhang, Q

author He, B. , author Zhang, Q. , author Tong, M. , author He, C. , year 2022 a. title An anchor-free method based on adaptive feature encoding and gaussian-guided sampling optimization for ship detection in sar imagery . journal Remote Sensing volume 14 , pages 1738

2022
[59]

, author Wang, W

author He, G. , author Wang, W. , author Shi, B. , author Liu, S. , author Xiang, H. , author Wang, X. , year 2022 b. title An improved yolo v4 algorithm-based object detection method for maritime vessels . journal International Journal of Science and Engineering Applications ...

2022 arXiv
[60]

, author Dai, Y

author He, Z. , author Dai, Y. , author Li, L. , author Xu, H. , author Jin, J. , author Liu, D. , year 2023 . title A coastal obstacle detection framework of dual usvs based on dual-view color fusion . journal Signal, Image and Video Processing volume 17 , pages 3883--3892

2023
[61]

, author Li, L

author He, Z. , author Li, L. , author Xu, H. , author Zong, L. , author Dai, Y. , year 2024 . title An obstacle detection method for dual usvs based on sgnn-rmen registration of dual-view point clouds . journal Ocean Engineering volume 292 , pages 116557

2024
[62]

, author Stahl, A

author Helgesen, .K. , author Stahl, A. , author Brekke, E.F. , year 2023 . title Maritime tracking with georeferenced multi-camera fusion . journal IEEE Access volume 11 , pages 30340--30359

2023
[63]

, author Vasstein, K

author Helgesen, .K. , author Vasstein, K. , author Brekke, E.F. , author Stahl, A. , year 2022 . title Heterogeneous multi-sensor tracking for an autonomous surface vehicle in a littoral environment . journal Ocean Engineering volume 252 , pages 111168

2022
[64]

, author Zhou, Y

author Hong, B. , author Zhou, Y. , author Qin, H. , author Wei, Z. , author Liu, H. , author Yang, Y. , year 2022 . title Few-shot object detection using multimodal sensor systems of unmanned surface vehicles . journal Sensors volume 22 , pages 1511

2022
[65]

, author Tang, Y

author Huang, Z. , author Tang, Y. , author Tang, C. , author Xu, Y. , author Luo, C. , author Liu, B. , author Zhang, H.T. , year 2023 . title Wdnet: A water depth perception network with global information , in: booktitle 2023 42nd Chinese Control Conference (CCC) , pp. page...

2023
[67]

, author Han, S

author Iandola, F.N. , author Han, S. , author Moskewicz, M.W. , author Ashraf, K. , author Dally, W.J. , author Keutzer, K. , year 2016 . title Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size . journal arXiv preprint arXiv:1602.07360

2016 arXiv
[68]

title Act on protection of personal information – appi

author Japan , year 2003 . title Act on protection of personal information – appi . howpublished https://www.ppc.go.jp/files/pdf/APPI_english.pdf

2003
[69]

, author Chadda, A

author Jeong, M. , author Chadda, A. , author Ren, Z. , author Zhao, L. , author Liu, H. , author Roznere, M. , author Zhang, A. , author Jiang, Y. , author Achong, S. , author Lensgraf, S. , et al., year 2024 . title Multi-modal perception dataset of in-water objects for auto...

2024 doi
[70]

, author Li, A.Q

author Jeong, M. , author Li, A.Q. , year 2021 . title Efficient lidar-based in-water obstacle detection and segmentation by autonomous surface vehicles in aquatic environments , in: booktitle 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp...

2021
[71]

, author Liu, L

author Jian, J. , author Liu, L. , author Zhang, Y. , author Xu, K. , author Yang, J. , year 2023 . title Optical remote sensing ship recognition and classification based on improved yolov5 . journal Remote Sensing volume 15 , pages 4319

2023
[72]

, author Su, L

author Jiang, Z. , author Su, L. , author Sun, Y. , year 2024 . title Yolov7-ship: A lightweight algorithm for ship object detection in complex marine environments . journal Journal of Marine Science and Engineering volume 12 , pages 190

2024
[73]

, author Liu, D

author Jin, J. , author Liu, D. , author Li, F. , author Dai, Y. , author Li, L. , author Ma, Y. , year 2024 . title Wide baseline stereovision based obstacle detection for unmanned surface vehicles . journal Signal, Image and Video Processing , pages 1--10

2024
[74]

, author Jung, C.H

author Kang, B.S. , author Jung, C.H. , year 2022 . title Detecting maritime obstacles using camera images . journal Journal of Marine Science and Engineering volume 10 , pages 1528 . :https://doi.org/10.3390/jmse10101528

2022 doi
[75]

, author Greidanus, H

author Kanjir, U. , author Greidanus, H. , author O s tir, K. , year 2018 . title Vessel detection and classification from spaceborne optical images: A literature survey . journal Remote sensing of environment volume 207 , pages 1--26 . :https://doi.org/10.1016/j.rse.2017.12.033

2018 doi
[77]

, author Liu, J

author Karangwa, J. , author Liu, J. , author Zeng, Z. , year 2023 b. title Vehicle detection for autonomous driving: A review of algorithms and datasets . journal IEEE Transactions on Intelligent Transportation Systems volume 24 , pages 11568--11594 . :https://doi.org/10.1109...

2023
[78]

, author Aziz, A

author Kaur, P. , author Aziz, A. , author Jain, D. , author Patel, H. , author Hirokawa, J. , author Townsend, L. , author Reimers, C. , author Hua, F. , year 2022 . title Sea situational awareness (seasaw) dataset , in: booktitle Proceedings of the IEEE/CVF Conference on Com...

2022
[80]

, author Kim, N

author Kim, J.H. , author Kim, N. , author Park, Y.W. , author Won, C.S. , year 2022 . title Object detection and classification based on yolo-v5 with improved maritime dataset . journal Journal of Marine Science and Engineering volume 10 . :https://doi.org/10.3390/rs13050988

2022 doi
[81]

, author Kim, J

author Kim, K. , author Kim, J. , author Kim, J. , year 2021 . title Robust data association for multi-object detection in maritime environments using camera and radar measurements . journal IEEE Robotics and Automation Letters volume 6 , pages 5865--5872

2021
[82]

, author Park, T.H

author Kim, T. , author Park, T.H. , year 2020 . title Extended kalman filter (ekf) design for vehicle position tracking using reliability function of radar and lidar . journal Sensors volume 20 , pages 4126

2020
[83]

, author Girshick, R

author Kirillov, A. , author Girshick, R. , author He, K. , author Doll \'a r, P. , year 2019 . title Panoptic feature pyramid networks , in: booktitle Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. pages 6399--6408 . :https://doi.org/1...

2019
[84]

, author Wu, Y

author Kirillov, A. , author Wu, Y. , author He, K. , author Girshick, R. , year 2020 . title Pointrend: Image segmentation as rendering , in: booktitle Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. pages 9799--9808 . :https://doi.org/...

2020
[85]

, author Racewicz, S

author Kozłowski, M. , author Racewicz, S. , author Wierzbicki, S. , year 2024 . title Image analysis in autonomous vehicles: A review of the latest ai solutions and their comparison . journal Applied Sciences volume 14 . :https://doi.org/10.3390/app14188150

2024 doi
[86]

, author Sulić Kenk, V

author Kristan, M. , author Sulić Kenk, V. , author Kovačič, S. , author Perš, J. , year 2016 . title Fast image-based obstacle detection from unmanned surface vehicles . journal IEEE Transactions on Cybernetics volume 46 , pages 641--654 . :https://doi.org/10.1109/TCYB.2015.2412251

2016
[87]

, author Chavez-Galaviz, J

author Lambert, R. , author Chavez-Galaviz, J. , author Li, J. , author Mahmoudian, N. , year 2022 . title Rosebud: A deep fluvial segmentation dataset for monocular vision-based river navigation and obstacle avoidance . journal Sensors volume 22 . :https://doi.org/10.3390/s22134681

2022 doi
[88]

, author Yu, X

author Lang, C. , author Yu, X. , author Rong, X. , year 2024 . title Lsdnet: a lightweight ship detection network with improved yolov7 . journal Journal of Real-Time Image Processing volume 21 , pages 1--14

2024
[89]

, author Roh, M.I

author Lee, S.J. , author Roh, M.I. , author Lee, H.W. , author Ha, J.S. , author Woo, I.G. , year 2018 . title Image-based ship detection and classification for unmanned surface vehicle using real-time object detection neural networks , in: booktitle ISOPE International Ocean...

2018
[90]

, author Zhu, X

author Li, A. , author Zhu, X. , author He, S. , author Xia, J. , year 2021 . title Water surface object detection using panoramic vision based on improved single-shot multibox detector . journal EURASIP Journal on Advances in Signal Processing volume 2021 , pages 1--15

2021
[91]

, author Chen, M

author Li, J. , author Chen, M. , author Hou, S. , author Wang, Y. , author Luo, Q. , author Wang, C. , year 2023 . title An improved s2a-net algorithm for ship object detection in optical remote sensing images . journal Remote Sensing volume 15 , pages 4559

2023
[92]

, author Guo, J

author Li, Y. , author Guo, J. , author Guo, X. , author Liu, K. , author Zhao, W. , author Luo, Y. , author Wang, Z. , year 2020 . title A novel target detection method of the unmanned surface vehicle under all-weather conditions with an improved yolov3 . journal Sensors volu...

2020 doi
[93]

, author Xu, H

author Liang, X. , author Xu, H. , author Cheng, Y. , year 2023 . title Visual temporal fusion based free space segmentation for autonomous surface vessels . journal arXiv preprint arXiv:2310.00879

2023 arXiv
[94]

, author Doll \'a r, P

author Lin, T.Y. , author Doll \'a r, P. , author Girshick, R. , author He, K. , author Hariharan, B. , author Belongie, S. , year 2017 . title Feature pyramid networks for object detection , in: booktitle Proceedings of the IEEE conference on computer vision and pattern recog...

2017
[95]

, author Liu, C

author Lin, X. , author Liu, C. , author Pattillo, A. , author Yu, M. , author Aloimonous, Y. , year 2023 . title Seadronesim: Simulation of aerial images for detection of objects above water , in: booktitle Proceedings of the IEEE/CVF Winter Conference on Applications of Comp...

2023
[96]

, author Rottmann, M

author Lis, K. , author Rottmann, M. , author Honari, S. , author Fua, P. , author Salzmann, M. , year 2022 . title Attentropy: Segmenting unknown objects in complex scenes using the spatial attention entropy of semantic segmentation transformers . journal arXiv preprint arXiv...

2022 arXiv
[97]

, author Zhang, J

author Liu, D. , author Zhang, J. , author Jin, J. , author Dai, Y. , author Li, L. , year 2022 . title A new approach of obstacle fusion detection for unmanned surface vehicle using dempster-shafer evidence theory . journal Applied Ocean Research volume 119 , pages 103016

2022
[98]

, author Nie, J

author Liu, H. , author Nie, J. , author Liu, Y. , author Wu, Y. , author Wang, H. , author Qu, F. , author Liu, W. , author Li, Y. , year 2020 . title A multi-modality sensor system for unmanned surface vehicle . journal Neural Processing Letters volume 52 , pages 977--992

2020
[99]

, author Li, H

author Liu, J. , author Li, H. , author Luo, J. , author Xie, S. , author Sun, Y. , year 2021 . title Efficient obstacle detection based on prior estimation network and spatially constrained mixture model for unmanned surface vehicles . journal Journal of Field Robotics volume...

2021 doi
[100]

, author Yurtsever, E

author Liu, M. , author Yurtsever, E. , author Fossaert, J. , author Zhou, X. , author Zimmer, W. , author Cui, Y. , author Zagar, B.L. , author Knoll, A.C. , year 2024 a. title A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook . jou...

2024
[101]

, author Fan, X

author Liu, S. , author Fan, X. , author Wu, G. , author Yao, L. , year 2024 b. title More modalities mean better: Vessel target recognition and localization through symbiotic transformer and multi-view regression . journal IEEE Transactions on Geoscience and Remote Sensing

2024
[102]

, author Zhang, W

author Liu, Z. , author Zhang, W. , author Yu, H. , author Zhou, S. , author Qi, W. , author Guo, Y. , author Li, C. , year 2023 . title Improved yolov5s for small ship detection with optical remote sensing images . journal IEEE Geoscience and Remote Sensing Letters

2023
[103]

, author Zhang, Y

author Liu, Z. , author Zhang, Y. , author Yu, X. , author Yuan, C. , year 2016 . title Unmanned surface vehicles: An overview of developments and challenges . journal Annual Reviews in Control volume 41 , pages 71--93 . :https://doi.org/10.1016/j.arcontrol.2016.04.018

2016 doi
[104]

, author Shelhamer, E

author Long, J. , author Shelhamer, E. , author Darrell, T. , year 2015 . title Fully convolutional networks for semantic segmentation , in: booktitle Proceedings of the IEEE conference on computer vision and pattern recognition , pp. pages 3431--3440 . :https://doi.org/10.110...

2015
[105]

, author Souza, R

author Lopes, A. , author Souza, R. , author Pedrini, H. , year 2022 . title A survey on rgb-d datasets . journal Computer Vision and Image Understanding volume 222 , pages 103489 . :https://doi.org/10.1016/j.cviu.2022.103489

2022
[106]

, author Kang, Z

author Ma, F. , author Kang, Z. , author Chen, C. , author Sun, J. , author Deng, J. , year 2023 . title Mrisnet: Robust ship instance segmentation in challenging marine radar environments . journal Journal of Marine Science and Engineering volume 12 , pages 72

2023
[107]

, author Yin, Y

author Ma, R. , author Yin, Y. , author Chen, J. , author Chang, R. , year 2024 a. title Multi-modal information fusion for lidar-based 3d object detection framework . journal Multimedia Tools and Applications volume 83 , pages 7995--8012

2024
[108]

, author Ouyang, W

author Ma, X. , author Ouyang, W. , author Simonelli, A. , author Ricci, E. , year 2024 b. title 3d object detection from images for autonomous driving: A survey . journal IEEE Transactions on Pattern Analysis and Machine Intelligence volume 46 , pages 3537--3556 . :https://do...

2024
[109]

, author Rastegari, M

author Mehta, S. , author Rastegari, M. , year 2021 . title Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer . journal ArXiv volume abs/2110.02178

2021 arXiv
[110]

, author Zeng, H

author Miao, T. , author Zeng, H. , author Wang, H. , author Yang, W. , year 2021 . title Inshore ship detection in sar images via an improved ssd model with wavelet decomposition , in: booktitle 2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) , pp. pages 1--5

2021
[111]

, author Nalamada, T

author Misra, D. , author Nalamada, T. , author Arasanipalai, A.U. , author Hou, Q. , year 2021 . title Rotate to attend: Convolutional triplet attention module , in: booktitle Proceedings of the IEEE/CVF winter conference on applications of computer vision , pp. pages 3139--3148

2021
[112]

, author Ebecken, N.F.F

author Moreira, R.D.S. , author Ebecken, N.F.F. , author Alves, A.S. , author Livernet, F. , author Campillo-Navetti, A. , year 2014 . title A survey on video detection and tracking of maritime vessels . journal International Journal of Recent Research and Applied Studies volume 20

2014
[113]

, author Bovcon, B

author Muhovi c , J. , author Bovcon, B. , author Kristan, M. , author Per s , J. , et al., year 2019 . title Obstacle tracking for unmanned surface vessels using 3-d point cloud . journal IEEE Journal of Oceanic Engineering volume 45 , pages 786--798

2019
[114]

, author Mandeljc, R

author Muhovič, J. , author Mandeljc, R. , author Bovcon, B. , author Kristan, M. , author Perš, J. , year 2020 . title Obstacle tracking for unmanned surface vessels using 3-d point cloud . journal IEEE Journal of Oceanic Engineering volume 45 , pages 786--798 . :10.1109/JOE....

2020
[115]

, author Cho, S.W

author Nanda, A. , author Cho, S.W. , author Lee, H. , author Park, J.H. , year 2024 . title Kolomverse: Korea open large-scale image dataset for object detection in the maritime universe . journal IEEE Transactions on Intelligent Transportation Systems , pages 1–9 :https://do...

2024
[116]

, author Jiang, Z

author Nie, S. , author Jiang, Z. , author Zhang, H. , author Cai, B. , author Yao, Y. , year 2018 . title Inshore ship detection based on mask r-cnn , in: booktitle IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium , organization IEEE . pp. pages 693...

2018
[117]

, author Zhao, L

author Ning, Y. , author Zhao, L. , author Zhang, C. , author Yuan, Z. , year 2024 . title Std-yolov5: a ship-type detection model based on improved yolov5 . journal Ships and Offshore Structures volume 19 , pages 66--75

2024
[118]

, author DeFilippo, M

author Nirgudkar, S. , author DeFilippo, M. , author Sacarny, M. , author Benjamin, M. , author Robinette, P. , year 2023 . title Massmind: Massachusetts maritime infrared dataset . journal The International Journal of Robotics Research volume 42 , pages 21--32 . :https://doi....

2023 doi
[119]

, author Fan, Y

author Niu, L. , author Fan, Y. , author Liu, T. , author Han, Q. , year 2024 . title Momt: A maritime real-time visual multi-object tracking algorithm based on unmanned surface vehicles . journal IEEE Sensors Journal , pages 1--1 :10.1109/JSEN.2024.3455572

2024
[120]

, author Bulat, A

author Pan, J. , author Bulat, A. , author Tan, F. , author Zhu, X. , author Dudziak, L. , author Li, H. , author Tzimiropoulos, G. , author Martinez, B. , year 2022 . title Edgevits: Competing light-weight cnns on mobile devices with vision transformers , in: booktitle Europe...

2022
[121]

, author Roh, M.I

author Park, J.H. , author Roh, M.I. , author Lee, H.W. , author Jo, Y.M. , author Ha, J. , author Son, N.S. , year 2024 . title Multi-vessel target tracking with camera fusion for unmanned surface vehicles . journal International Journal of Naval Architecture and Ocean Engine...

2024
[122]

, author Rajan, D

author Prasad, D.K. , author Rajan, D. , author Rachmawati, L. , author Rajabally, E. , author Quek, C. , year 2017 . title Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey . journal IEEE Transactions on Intell...

2017
[123]

, author Yi, L

author Qi, C.R. , author Yi, L. , author Su, H. , author Guibas, L.J. , year 2017 . title Pointnet++: Deep hierarchical feature learning on point sets in a metric space , in: booktitle Advances in Neural Information Processing Systems

2017
[124]

, author Li, B

author Qi, L. , author Li, B. , author Chen, L. , author Wang, W. , author Dong, L. , author Jia, X. , author Huang, J. , author Ge, C. , author Xue, G. , author Wang, D. , year 2019 . title Ship target detection algorithm based on improved faster r-cnn . journal Electronics v...

2019 doi
[125]

, author Lai, X

author Qian, R. , author Lai, X. , author Li, X. , year 2022 . title 3d object detection for autonomous driving: A survey . journal Pattern Recognition volume 130 , pages 108796 . :https://doi.org/10.1016/j.patcog.2022.108796

2022
[126]

, author Liu, G

author Qiao, D. , author Liu, G. , author Li, W. , author Lyu, T. , author Zhang, J. , year 2022 . title Automated full scene parsing for marine asvs using monocular vision . journal Journal of Intelligent & Robotic Systems volume 104 , pages 37 . :https://doi.org/10.1007/s108...

2022 doi
[127]

, author Liu, G

author Qiao, D. , author Liu, G. , author Lv, T. , author Li, W. , author Zhang, J. , year 2021 . title Marine vision-based situational awareness using discriminative deep learning: A survey . journal Journal of Marine Science and Engineering volume 9 , pages 397 . :https://do...

2021 doi
[128]

, author Yin, J

author Qiao, Y. , author Yin, J. , author Wang, W. , author Duarte, F. , author Yang, J. , author Ratti, C. , year 2023 . title Survey of deep learning for autonomous surface vehicles in marine environments . journal IEEE Transactions on Intelligent Transportation Systems volu...

2023
[129]

, author Xu, L

author Rekavandi, A.M. , author Xu, L. , author Boussaid, F. , author Seghouane, A.K. , author Hoefs, S. , author Bennamoun, M. , year 2022 . title A guide to image and video based small object detection using deep learning: Case study of maritime surveillance . journal arXiv ...

2022 doi arXiv
[130]

, author Fischer, P

author Ronneberger, O. , author Fischer, P. , author Brox, T. , year 2015 . title U-net: Convolutional networks for biomedical image segmentation , in: booktitle Medical image computing and computer-assisted intervention--MICCAI 2015: 18th international conference, Munich, Ger...

2015 doi
[131]

, author Ortiz-Perez, D

author Ruiz-Ponce, P. , author Ortiz-Perez, D. , author Garcia-Rodriguez, J. , author Kiefer, B. , year 2023 . title Poseidon: A data augmentation tool for small object detection datasets in maritime environments . journal Sensors volume 23 , pages 3691 . :https://doi.org/10.3...

2023 doi
[132]

, author Hardianti, A

author Santosa, A. , author Hardianti, A. , author Hasugian, S. , author Sutrisno, I. , author Khumaidi, A. , year 2021 . title Safe distance reminder system on ship against port for the standing process using image processing , in: booktitle IOP Conference Series: Materials S...

2021
[133]

, year 1976

author Shafer, G. , year 1976 . title A mathematical theory of evidence . volume volume 42

1976
[134]

, author Dadashpour, I

author Shahedi, A. , author Dadashpour, I. , author Rezaei, M. , year 2023 . title Barriers to the sustainable adoption of autonomous vehicles in developing countries: A multi-criteria decision-making approach . journal Heliyon volume 9 , pages e15975 . :https://doi.org/10.101...

2023 doi
[135]

, author Wang, J

author Shao, Z. , author Wang, J. , author Deng, L. , author Huang, X. , author Lu, T. , author Luo, F. , author Zhang, R. , author Lv, X. , author Dang, C. , author Ding, Q. , et al., year 2021 . title Glsd: The global large-scale ship database and baseline evaluations . jour...

2021 arXiv
[136]

, author Wu, W

author Shao, Z. , author Wu, W. , author Wang, Z. , author Du, W. , author Li, C. , year 2018 . title Seaships: A large-scale precisely annotated dataset for ship detection . journal IEEE Transactions on Multimedia volume 20 , pages 2593--2604 . :https://doi.org/10.1109/TMM.20...

2018
[137]

, author Saqib, M

author Sharma, R. , author Saqib, M. , author Lin, C. , author Blumenstein, M. , year 2024 . title Massnet: Multiscale attention for single-stage ship instance segmentation . journal Neurocomputing volume 594 , pages 127830 . :https://doi.org/10.1016/j.neucom.2024.127830

2024
[138]

, author Song, B

author Si, J. , author Song, B. , author Wu, J. , author Lin, W. , author Huang, W. , author Chen, S. , year 2023 . title Maritime ship detection method for satellite images based on multiscale feature fusion . journal IEEE Journal of Selected Topics in Applied Earth Observati...

2023
[139]

, author Farahnakian, F

author Soloviev, V. , author Farahnakian, F. , author Zelioli, L. , author Iancu, B. , author Lilius, J. , author Heikkonen, J. , year 2020 . title Comparing cnn-based object detectors on two novel maritime datasets , in: booktitle 2020 IEEE international conference on multime...

2020
[140]

, author Li, T

author Song, R. , author Li, T. , author Li, T. , year 2023 . title Ship detection in haze and low-light remote sensing images via colour balance and dcnn . journal Applied Ocean Research volume 139 , pages 103702

2023
[141]

, author Bloisi, D

author Steccanella, L. , author Bloisi, D. , author Castellini, A. , author Farinelli, A. , year 2020 . title Waterline and obstacle detection in images from low-cost autonomous boats for environmental monitoring . journal Robotics and Autonomous Systems volume 124 , pages 103...

2020
[142]

, author Garcia, R

author Strudel, R. , author Garcia, R. , author Laptev, I. , author Schmid, C. , year 2021 . title Segmenter: Transformer for semantic segmentation , in: booktitle Proceedings of the IEEE/CVF international conference on computer vision , pp. pages 7262--7272 . :https://doi.org...

2021
[143]

, author Chen, Y

author Su, L. , author Chen, Y. , author Song, H. , author Li, W. , year 2023 . title A survey of maritime vision datasets . journal Multimedia Tools and Applications volume 82 , pages 28873--28893 . :https://doi.org/10.1007/s11042-023-14756-9

2023 doi
[144]

, author Su, L

author Sun, Y. , author Su, L. , author Cui, H. , author Chen, Y. , author Yuan, S. , year 2021 . title Ship instance segmentation in foggy scene , in: booktitle 2021 40th Chinese Control Conference (CCC) , pp. pages 8340--8345

2021
[145]

, author Su, L

author Sun, Y. , author Su, L. , author Luo, Y. , author Meng, H. , author Li, W. , author Zhang, Z. , author Wang, P. , author Zhang, W. , year 2022 a. title Global mask r-cnn for marine ship instance segmentation . journal Neurocomputing volume 480 , pages 257--270 . :https:...

2022 doi
[146]

, author Su, L

author Sun, Y. , author Su, L. , author Luo, Y. , author Meng, H. , author Zhang, Z. , author Zhang, W. , author Yuan, S. , year 2022 b. title Irdclnet: Instance segmentation of ship images based on interference reduction and dynamic contour learning in foggy scenes . journal ...

2022
[147]

, author Su, L

author Sun, Y. , author Su, L. , author Yuan, S. , author Meng, H. , year 2023 a. title Cascade aggregation network for ship instance segmentation , in: booktitle 2023 9th International Conference on Control, Automation and Robotics (ICCAR) , pp. pages 355--359

2023
[148]

, author Su, L

author Sun, Y. , author Su, L. , author Yuan, S. , author Meng, H. , year 2023 b. title Danet: Dual-branch activation network for small object instance segmentation of ship images . journal IEEE Transactions on Circuits and Systems for Video Technology

2023
[149]

, author Meng, C

author Sun, Z. , author Meng, C. , author Huang, T. , author Zhang, Z. , author Chang, S. , year 2023 c. title Marine ship instance segmentation by deep neural networks using a global and local attention (gala) mechanism . journal Plos one volume 18 , pages e0279248 . :https:/...

2023 doi
[150]

, author Passalis, N

author Taipalmaa, J. , author Passalis, N. , author Zhang, H. , author Gabbouj, M. , author Raitoharju, J. , year 2019 . title High-resolution water segmentation for autonomous unmanned surface vehicles: a novel dataset and evaluation , in: booktitle 2019 IEEE 29th Internation...

2019
[151]

, year 2018

author Taxonomy, S. , year 2018 . title Definitions for terms related to driving automation systems for on-road motor vehicles . journal SAE: Warrendale, PA, USA volume 3016

2018
[152]

, author Meng, H

author Tian, Y. , author Meng, H. , author Yuan, F. , year 2023 a. title Multiscale and multilevel enhanced features for ship target recognition in complex environments . journal IEEE Transactions on Industrial Informatics

2023
[153]

, author Meng, H

author Tian, Y. , author Meng, H. , author Yuan, F. , author Ling, Y. , author Yuan, N. , year 2023 b. title Vision transformer with enhanced self-attention for few shot ship target recognition in complex environments . journal IEEE Transactions on Instrumentation and Measurement

2023
[154]

, author Anwar, A

author Trinh, L. , author Anwar, A. , author Mercelis, S. , year 2024 a. title Data selection method for assessment of autonomous vehicles . journal arXiv preprint arXiv:2407.12065 :https://doi.org/10.48550/ARXIV.2407.12065

2024 doi arXiv
[155]

, author Anwar, A

author Trinh, L. , author Anwar, A. , author Mercelis, S. , year 2024 b. title Multiple data sources and domain generalization learning method for road surface defect classification . journal arXiv preprint arXiv:2407.10197 :https://doi.org/10.48550/ARXIV.2407.10197

2024 doi arXiv
[156]

, author Anwar, A

author Trinh, L. , author Anwar, A. , author Mercelis, S. , year 2024 c. title Seadsc: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles , in: booktitle Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vi...

2024
[157]

, author Ha, B

author Trinh, L. , author Ha, B. , author Tran, A.T. , year 2023 a. title Fisheyepp4av: A privacy-preserving method for autonomous vehicles on fisheye camera images , in: booktitle 2023 RIVF International Conference on Computing and Communication Technologies (RIVF) , pp. page...

2023
[158]

, author Pham, P

author Trinh, L. , author Pham, P. , author Trinh, H. , author Bach, N. , author Nguyen, D. , author Nguyen, G. , author Nguyen, H. , year 2023 b. title Pp4av: A benchmarking dataset for privacy-preserving autonomous driving , in: booktitle Proceedings of the IEEE/CVF Winter C...

2023
[159]

title Review of Maritime Transport 2021

author UNCTD , year 2021 . title Review of Maritime Transport 2021 . :https://doi.org/10.18356/9789210000970

2021 doi
[160]

, author Yeong, D.J

author Velasco-Hernandez, G. , author Yeong, D.J. , author Barry, J. , author Walsh, J. , year 2020 . title Autonomous driving architectures, perception and data fusion: A review , in: booktitle 2020 IEEE 16th International Conference on Intelligent Computer Communication and ...

2020
[161]

, author Jiang, P

author Wang, B. , author Jiang, P. , author Gao, J. , author Huo, W. , author Yang, Z. , author Liao, Y. , year 2023 a. title A lightweight few-shot marine object detection network for unmanned surface vehicles . journal Ocean Engineering volume 277 , pages 114329 . :https://d...

2023
[162]

, author Wang, W

author Wang, H. , author Wang, W. , author Liu, J. , year 2021 . title Temporal memory attention for video semantic segmentation , in: booktitle 2021 IEEE International Conference on Image Processing (ICIP) , pp. pages 2254--2258 . :https://doi.org/10.1109/icip42928.2021.9506731

2021
[163]

, author Chen, T

author Wang, N. , author Chen, T. , author Liu, S. , author Wang, R. , author Karimi, H.R. , author Lin, Y. , year 2023 b. title Deep learning-based visual detection of marine organisms: A survey . journal Neurocomputing volume 532 , pages 1--32 . :https://doi.org/10.1016/j.ne...

2023 doi
[164]

, author Wang, Y

author Wang, N. , author Wang, Y. , author Feng, Y. , author Wei, Y. , year 2024 a. title Mdd-shipnet: Math-data integrated defogging for fog-occlusion ship detection . journal IEEE Transactions on Intelligent Transportation Systems

2024
[165]

, author Wang, Y

author Wang, N. , author Wang, Y. , author Wei, Y. , author Han, B. , author Feng, Y. , year 2024 b. title Marine vessel detection dataset and benchmark for unmanned surface vehicles . journal Applied Ocean Research volume 142 , pages 103835 . :https://doi.org/10.1016/j.apor.2...

2024
[166]

, author Qiu, S

author Wang, S. , author Qiu, S. , author Sun, Z. , author Hsieh, T.H. , author Qian, F. , author Xiao, Y. , year 2024 c. title Single-view 3d object perception based on vessel generative adversarial network for autonomous ships . journal IEEE Transactions on Intelligent Trans...

2024
[167]

, author Wang, L

author Wang, Y. , author Wang, L. , author Jiang, Y. , author Li, T. , year 2020 . title Detection of self-build data set based on yolov4 network , in: booktitle 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) , pp. pages 64...

2020
[168]

, author Jiang, X

author Wei, Q. , author Jiang, X. , author Liu, Y. , author Su, Q. , author Yu, M. , year 2024 . title Small object detection on the water surface based on radar and camera fusion , in: booktitle ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Pr...

2024
[169]

, author Cao, P

author Wen, G. , author Cao, P. , author Wang, H. , author Chen, H. , author Liu, X. , author Xu, J. , author Zaiane, O. , year 2023 . title Ms-ssd: Multi-scale single shot detector for ship detection in remote sensing images . journal Applied Intelligence volume 53 , pages 1586--1604

2023
[170]

, author Li, X

author Wu, W. , author Li, X. , author Hu, Z. , author Liu, X. , year 2023 . title Ship detection and recognition based on improved yolov7 . journal Comput. Mater. Contin volume 76 , pages 489--498

2023
[171]

, author Wang, W

author Xie, E. , author Wang, W. , author Yu, Z. , author Anandkumar, A. , author Alvarez, J.M. , author Luo, P. , year 2021 . title Segformer: Simple and efficient design for semantic segmentation with transformers . journal Advances in neural information processing systems v...

2021
[172]

, author Geng, Z

author Xu, H. , author Geng, Z. , author He, J. , author Shi, Y. , author Yu, Y. , author Zhang, X. , year 2023 a. title A multi-task water surface visual perception network for unmanned surface vehicles , in: booktitle 2023 IEEE International Conference on Signal Processing, ...

2023
[173]

, author Zhang, X

author Xu, H. , author Zhang, X. , author He, J. , author Geng, Z. , author Pang, C. , author Yu, Y. , year 2024 a. title Surround-view water surface bev segmentation for autonomous surface vehicles: Dataset, baseline and hybrid-bev network . journal IEEE Transactions on Intel...

2024
[174]

, author Zhang, X

author Xu, H. , author Zhang, X. , author He, J. , author Geng, Z. , author Pang, C. , author Yu, Y. , year 2024 b. title Surround-view water surface bev segmentation for autonomous surface vehicles: Dataset, baseline and hybrid-bev network . journal IEEE Transactions on Intel...

2024
[175]

, author Zhang, X

author Xu, H. , author Zhang, X. , author He, J. , author Yu, Y. , author Cheng, Y. , year 2024 c. title Real-time volumetric perception for unmanned surface vehicles through fusion of radar and camera . journal IEEE Transactions on Instrumentation and Measurement

2024
[176]

, author Xiong, Z

author Xu, J. , author Xiong, Z. , author Bhattacharyya, S.P. , year 2023 b. title Pidnet: A real-time semantic segmentation network inspired by pid controllers , in: booktitle Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. pages 19529--19539

2023
[177]

, author Yan, M

author Xu, Y. , author Yan, M. , author Xu, C. , author Zhang, H. , author Liu, Y. , author Xu, X. , year 2021 . title Adaptive selecting and learning network and a new benchmark for imbalanced fine-grained ship classification . journal IEEE Access volume 9 , pages 58116--58126

2021
[178]

, author Chen, X

author Xue, H. , author Chen, X. , author Zhang, R. , author Wu, P. , author Li, X. , author Liu, Y. , year 2021 . title Deep learning-based maritime environment segmentation for unmanned surface vehicles using superpixel algorithms . journal Journal of Marine Science and Engi...

2021
[179]

, author Solihin, M.I

author Yang, D. , author Solihin, M.I. , author Zhao, Y. , author Yao, B. , author Chen, C. , author Cai, B. , author Machmudah, A. , year 2024 a. title A review of intelligent ship marine object detection based on rgb camera . journal IET Image Processing volume 18 , pages 28...

2024 doi
[180]

, author She, H

author Yang, X. , author She, H. , author Lou, M. , author Ye, H. , author Guan, J. , author Li, J. , author Xiang, Z. , author Shen, H. , author Zhang, B. , year 2024 b. title A joint ship detection and waterway segmentation method for environment-aware of usvs in canal water...

2024
[181]

, author She, H

author Yang, X. , author She, H. , author Lou, M. , author Ye, H. , author Guan, J. , author Li, J. , author Xiang, Z. , author Shen, H. , author Zhang, B. , year 2024 c. title A joint ship detection and waterway segmentation method for environment-aware of usvs in canal water...

2024
[182]

, author Chen, P

author Yang, Y. , author Chen, P. , author Ding, K. , author Chen, Z. , author Hu, K. , year 2023 a. title Object detection of inland waterway ships based on improved ssd model . journal Ships and Offshore Structures volume 18 , pages 1192--1200

2023
[183]

, author Jia, X

author Yang, Z. , author Jia, X. , author Li, H. , author Yan, J. , year 2023 b. title Llm4drive: A survey of large language models for autonomous driving . journal CoRR volume abs/2311.01043 . :https://doi.org/10.48550/ARXIV.2311.01043

2023 doi arXiv
[184]

, author Yang, J

author Yao, B. , author Yang, J. , author Ren, Y. , author Zhang, Q. , author Guo, Z. , year 2019 . title Research and comparison of ship classification algorithms based on variant cnns , in: booktitle 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation...

2019
[185]

, author Kanoulas, D

author Yao, L. , author Kanoulas, D. , author Ji, Z. , author Liu, Y. , year 2021 . title Shorelinenet: An efficient deep learning approach for shoreline semantic segmentation for unmanned surface vehicles , in: booktitle 2021 IEEE/RSJ International Conference on Intelligent R...

2021
[186]

, author Guan, R

author Yao, S. , author Guan, R. , author Wu, Z. , author Ni, Y. , author Huang, Z. , author Wen Liu, R. , author Yue, Y. , author Ding, W. , author Gee Lim, E. , author Seo, H. , author Lok Man, K. , author Ma, J. , author Zhu, X. , author Yue, Y. , year 2024 . title Watersce...

2024
[187]

, author Jalal, A

author Yoon, S. , author Jalal, A. , author Cho, J. , year 2023 . title Modan: Multifocal object detection associative network for maritime horizon surveillance . journal Journal of Marine Science and Engineering volume 11 , pages 1890 . :https://doi.org/10.3390/jmse11101890

2023 doi
[188]

, author Gao, C

author Yu, C. , author Gao, C. , author Wang, J. , author Yu, G. , author Shen, C. , author Sang, N. , year 2021 a. title Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation . journal International Journal of Computer Vision volume 129 , p...

2021 doi
[189]

, author Wang, J

author Yu, C. , author Wang, J. , author Peng, C. , author Gao, C. , author Yu, G. , author Sang, N. , year 2018 . title Bisenet: Bilateral segmentation network for real-time semantic segmentation , in: booktitle Proceedings of the European conference on computer vision (ECCV)...

2018 doi
[190]

, author Ren, H

author Yu, N. , author Ren, H. , author Deng, T. , author Fan, X. , year 2023 . title A lightweight radar ship detection framework with hybrid attentions . journal Remote Sensing volume 15 , pages 2743

2023
[191]

, author Wang, B

author Yu, Q. , author Wang, B. , author Su, Y. , year 2021 b. title Object detection-tracking algorithm for unmanned surface vehicles based on a radar-photoelectric system . journal IEEE Access volume 9 , pages 57529--57541 . :10.1109/ACCESS.2021.3072897

2021
[192]

, author Wang, L

author Yuan, Y. , author Wang, L. , author Wang, Y. , year 2021 . title Csanet for video semantic segmentation with inter-frame mutual learning . journal IEEE Signal Processing Letters volume 28 , pages 1675--1679 . :https://doi.org/10.1109/lsp.2021.3103666

2021
[193]

, author Sham, C.W

author Yue, Z. , author Sham, C.W. , author Lo, C.Y. , author Cheung, W. , author Yiu, C.Y. , year 2023 . title Sea view extension for semantic segmentation in cityscapes , in: booktitle 2023 9th International Conference on Applied System Innovation (ICASI) , pp. pages 33--35

2023
[194]

, author Shapiai, M.I

author Zakaria, N.J. , author Shapiai, M.I. , author Ghani, R.A. , author Yassin, M.N.M. , author Ibrahim, M.Z. , author Wahid, N. , year 2023 . title Lane detection in autonomous vehicles: A systematic review . journal IEEE Access volume 11 , pages 3729--3765 . :https://doi.o...

2023
[195]

, author Wang, C

author Zhang, D. , author Wang, C. , author Fu, Q. , year 2023 a. title Ofcos: an oriented anchor-free detector for ship detection in remote sensing images . journal IEEE Geoscience and Remote Sensing Letters volume 20 , pages 1--5

2023
[196]

, author Gao, J

author Zhang, J. , author Gao, J. , author Liang, J. , author Wu, Y. , author Li, B. , author Zhai, Y. , author Li, X. , year 2023 b. title Efficient water segmentation with transformer and knowledge distillation for usvs . journal Journal of Marine Science and Engineering vol...

2023
[197]

, author Du, X

author Zhang, L. , author Du, X. , author Zhang, R. , author Zhang, J. , year 2023 c. title A lightweight detection algorithm for unmanned surface vehicles based on multi-scale feature fusion . journal Journal of Marine Science and Engineering volume 11 , pages 1392

2023
[198]

, author Choi, J

author Zhang, M.M. , author Choi, J. , author Daniilidis, K. , author Wolf, M.T. , author Kanan, C. , year 2015 . title Vais: A dataset for recognizing maritime imagery in the visible and infrared spectrums , in: booktitle 2015 IEEE Conference on Computer Vision and Pattern Re...

2015
[199]

, author Shan, Y

author Zhang, Q. , author Shan, Y. , author Zhang, Z. , author Lin, H. , author Zhang, Y. , author Huang, K. , year 2023 d. title Multisensor fusion-based maritime ship object detection method for autonomous surface vehicles . journal Journal of Field Robotics

2023
[200]

, author Li, S

author Zhang, R. , author Li, S. , author Ji, G. , author Zhao, X. , author Li, J. , author Pan, M. , year 2021 a. title Survey on deep learning-based marine object detection . journal Journal of Advanced Transportation volume 2021 , pages 1--18 . :https://doi.org/10.1155/2021/5808206

2021 doi
[201]

, author Cai, T

author Zhang, S. , author Cai, T. , author Chen, Y. , year 2021 b. title Low light image enhancement in usv imaging system via u-net and attention mechanism , in: booktitle Proceedings of the 2021 13th International Conference on Machine Learning and Computing , pp. pages 342--348

2021
[202]

, author Wu, R

author Zhang, S. , author Wu, R. , author Xu, K. , author Wang, J. , author Sun, W. , year 2019 . title R-cnn-based ship detection from high resolution remote sensing imagery . journal Remote Sensing volume 11 , pages 631 . :https://doi.org/10.3390/rs11060631

2019 doi
[203]

, author He, X

author Zhang, W. , author He, X. , author Li, W. , author Zhang, Z. , author Luo, Y. , author Su, L. , author Wang, P. , year 2020 a. title An integrated ship segmentation method based on discriminator and extractor . journal Image and Vision Computing volume 93 , pages 103824...

2020 doi
[204]

, author Jiang, F

author Zhang, W. , author Jiang, F. , author Yang, C.F. , author Wang, Z.P. , author Zhao, T.J. , year 2021 c. title Research on unmanned surface vehicles environment perception based on the fusion of vision and lidar . journal IEEE Access volume 9 , pages 63107--63121

2021
[205]

, author Pang, J

author Zhang, W. , author Pang, J. , author Chen, K. , author Loy, C.C. , year 2021 d. title K-net: Towards unified image segmentation . journal Advances in Neural Information Processing Systems volume 34 , pages 10326--10338

2021
[206]

, author Yang, C.f

author Zhang, W. , author Yang, C.f. , author Jiang, F. , author Gao, X.z. , author Yang, K. , year 2020 b. title A water surface moving target detection based on information fusion using deep learning , in: booktitle Journal of Physics: Conference Series , p. pages 012020

2020
[207]

, author Li, C

author Zhang, Y. , author Li, C. , author Shang, S. , author Chen, X. , year 2023 e. title Swinseg: Swin transformer and mlp hybrid network for ship segmentation in maritime surveillance system . journal Ocean Engineering volume 281 , pages 114885

2023
[208]

, author Sun, J

author Zhang, Y. , author Sun, J. , author Chen, M. , author Wang, Q. , year 2022 . title Efficient semantic segmentation backbone evaluation for unmanned surface vehicles based on likelihood distribution estimation , in: booktitle 2022 18th International Conference on Mobilit...

2022
[209]

, author Yao, S

author Zhang, Z. , author Yao, S. , author Huang, Z. , author Wu, Z. , author Zhu, X. , author Yue, Y. , author Ma, J. , year 2024 . title Rv-yolox: Object detection on inland waterways based on optimized yolox through fusion of vision and 3+ 1d millimeter wave radar . journal...

2024
[210]

, author Zhang, H

author Zhao, H. , author Zhang, H. , author Zhao, Y. , year 2023 a. title Yolov7-sea: Object detection of maritime uav images based on improved yolov7 , in: booktitle Proceedings of the IEEE/CVF winter conference on applications of computer vision , pp. pages 233--238 . :https...

2023
[211]

, author Qiu, S

author Zhao, L. , author Qiu, S. , author Chen, Y. , year 2024 . title Enhanced water surface object detection with dynamic task-aligned sample assignment and attention mechanisms . journal Sensors volume 24 , pages 3104

2024
[212]

, author Syafrudin, M

author Zhao, W. , author Syafrudin, M. , author Fitriyani, N.L. , year 2023 b. title Cras-yolo: A novel multi-category vessel detection and classification model based on yolov5s algorithm . journal IEEE Access volume 11 , pages 11463--11478

2023
[213]

, author Zhong, Z

author Zhao, Y. , author Zhong, Z. , author Yang, F. , author Luo, Z. , author Lin, Y. , author Li, S. , author Sebe, N. , year 2021 . title Learning to generalize unseen domains via memory-based multi-source meta-learning for person re-identification , in: booktitle Proceedin...

2021
[214]

, author Wang, X

author Zheng, X. , author Wang, X. , author Luo, Z. , author Guo, B. , year 2020 . title Camera imaging calibration optimization for stereo vision system of marine unmanned ship . journal Journal of Coastal Research volume 111 , pages 288--292

2020
[215]

, author Zhang , S

author Zheng , Y. , author Zhang , S. , year 2020 . title Mcships: A large-scale ship dataset for detection and fine-grained categorization in the wild , in: booktitle 2020 IEEE International Conference on Multimedia and Expo (ICME) , pp. pages 1--6 . :https://doi.org/10.1109/...

2020
[216]

, author Zhang, Y

author Zheng, Y. , author Zhang, Y. , author Qian, L. , author Zhang, X. , author Diao, S. , author Liu, X. , author Cao, J. , author Huang, H. , year 2023 . title A lightweight ship target detection model based on improved yolov5s algorithm . journal Plos one volume 18 , page...

2023
[218]

, author Cheng, C

author Zhou, G.C. , author Cheng, C. , author Chen, Y.z. , year 2024 . title Efficient multi-branch segmentation network for situation awareness in autonomous navigation . journal Ocean Engineering volume 302 , pages 117741

2024
[219]

, author Jiang, P

author Zhou, J. , author Jiang, P. , author Zou, A. , author Chen, X. , author Hu, W. , year 2021 a. title Ship target detection algorithm based on improved yolov5 . journal Journal of Marine Science and Engineering volume 9 , pages 908 . :https://doi.org/10.3390/jmse9080908

2021 doi
[220]

, author Gao, Y

author Zhou, R. , author Gao, Y. , author Wu, P. , author Zhao, X. , author Dou, W. , author Sun, C. , author Zhong, Y. , author Wang, Y. , year 2022 . title Collision-free waterway segmentation for inland unmanned surface vehicles . journal IEEE Transactions on Instrumentatio...

2022
[221]

, author Sun, J

author Zhou, Z. , author Sun, J. , author Yu, J. , author Liu, K. , author Duan, J. , author Chen, L. , author Chen, C.P. , year 2021 b. title An image-based benchmark dataset and a novel object detector for water surface object detection . journal Frontiers in Neurorobotics v...

2021
[222]

, author Wu, W

author Zhu, Z. , author Wu, W. , author Wang, H. , author Li, H. , author He, Y. , author Liu, Y. , author Lu, Q. , author Zhan, X. , year 2023 . title Ldanet: the laplace-guided detail-constrained asymmetric network for real-time semantic segmentation . journal Multimedia Too...

2023
[223]

, author Khan, S

author Zunair, H. , author Khan, S. , author Hamza, A.B. , year 2024 . title Rsud20k: a dataset for road scene understanding in autonomous driving , in: booktitle 2024 IEEE International Conference on Image Processing (ICIP) , publisher IEEE . p. pages 708–714 . :https://doi.o...

2024
[224]

, author Kristan, M

author Z ust, L. , author Kristan, M. , year 2022 . title Temporal context for robust maritime obstacle detection , in: booktitle 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . :https://doi.org/10.1109/iros47612.2022.9982043

2022
[225]

, author Per s , J

author Z ust, L. , author Per s , J. , author Kristan, M. , year 2023 . title Lars: A diverse panoptic maritime obstacle detection dataset and benchmark , in: booktitle Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. pages 20304--20314 . :https://...

2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

[2] [2]

, author Michaelides, M.P

author Aslam, S. , author Michaelides, M.P. , author Herodotou, H. , year 2020 . title Internet of ships: A survey on architectures, emerging applications, and challenges . journal IEEE Internet of Things journal volume 7 , pages 9714--9727 . :https://doi.org/10.1109/jiot.2020.2993411

arXiv 2020

[3] [3]

, author Kanarachos, S

author Batsch, F. , author Kanarachos, S. , author Cheah, M. , author Ponticelli, R. , author Blundell, M. , year 2022 . title A taxonomy of validation strategies to ensure the safe operation of highly automated vehicles . journal Journal of Intelligent Transportation Systems volume 26 , pages 14--33 . :https://doi.org/10.1080/15472450.2020.1738231

arXiv 2022

[4] [4]

, author Janssens, R

author Billast, M. , author Janssens, R. , author Vanneste, A. , author Vanneste, S. , author Vasseur, O. , author Anwar, A. , author Mets, K. , author De Schepper, T. , author Oramas, J. , author Latr \'e , S. , et al., year 2022 . title Object detection to enable autonomous vessels on european inland waterways , in: booktitle IECON 2022--48th Annual Con...

arXiv 2022

[5] [5]

, author Iocchi, L

author Bloisi, D.D. , author Iocchi, L. , author Pennisi, A. , author Tombolini, L. , year 2015 . title Argos-venice boat classification , in: booktitle 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) , pp. pages 1--6 . :https://doi.org/10.1109/AVSS.2015.7301727

arXiv 2015

[6] [6]

, author Xiaoyang, X

author Bo, L. , author Xiaoyang, X. , author Xingxing, W. , author Wenting, T. , year 2021 . title Ship detection and classification from optical remote sensing images: A survey . journal Chinese Journal of Aeronautics volume 34 , pages 145--163 . :https://doi.org/10.1016/j.cja.2020.09.022

work page doi:10.1016/j.cja.2020.09.022 2021

[7] [7]

, author Kristan, M

author Bovcon, B. , author Kristan, M. , year 2021 . title Wasr—a water segmentation and refinement maritime obstacle detection network . journal IEEE Transactions on Cybernetics volume 52 , pages 12661--12674 . :https://doi.org/10.1109/tcyb.2021.3085856

arXiv 2021

[8] [8]

, author Mandeljc, R

author Bovcon, B. , author Mandeljc, R. , author Perš, J. , author Kristan, M. , year 2018 . title Stereo obstacle detection for unmanned surface vehicles by imu-assisted semantic segmentation . journal Robotics and Autonomous Systems volume 104 , pages 1--13 . :https://doi.org/10.1016/j.robot.2018.02.017

work page doi:10.1016/j.robot.2018.02.017 2018