Pith. sign in

REVIEW 3 major objections 6 minor 1 cited by

Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This survey claims to be the first robustness-focused overview of sonar deep learning, reporting zero papers on neural network verification and only a handful each on adversarial attacks, out-of-distribution detection, and uncertainty…

desk verdict A genuinely useful consolidation of sonar-DL datasets, simulators, and robustness work; the 'first robustness survey' claim is weaker than advertised, but the practical value survives that. read the letter →

arxiv 2412.11840 v1 pith:ZBM2MH5U submitted 2024-12-16 cs.RO cs.CVeess.SP

Martin Aubard , Ana Madureira , Luís Teixeira , José Pinto This is my paper · ORCID
classification cs.ROcs.CVeess.SP
keywords sonar-baseddeeplearningautonomousunderwatervehiclesrobustnessneuralnetworkverificationout-of-distributiondetectionadversarialattacksuncertaintyquantificationsonardatasets
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

The paper sets out to be the first survey that treats sonar-based deep learning through the lens of reliability rather than just model accuracy. It assembles the state of the art in sonar perception tasks, along with 19 open datasets and underwater simulators, and then tabulates how much robustness research exists. Its central finding is that the area is almost empty: no neural network verification studies for sonar, four adversarial-attack studies, four out-of-distribution studies, and two uncertainty-quantification studies. The authors argue this matters because autonomous underwater vehicles increasingly rely on real-time sonar deep learning for navigation, and noisy, variable sonar data can silently break a model. The paper closes with a pre-deployment workflow intended to make sonar deep learning safe enough to trust.

What carries the argument

The organizing device is a four-part robustness lens, namely neural network verification, adversarial attacks, out-of-distribution detection, and uncertainty quantification, applied across four systematic comparisons: prior surveys, sonar datasets, underwater simulators, and existing robustness publications. The prescriptive output is the proposed pre-deployment workflow, which chains transfer learning, data augmentation, out-of-distribution and uncertainty checks, and either neural network verification or adversarial testing before a model is allowed to drive an autonomous vehicle.

What would settle it

A systematic literature search for sonar combined with neural network verification before this paper's submission would falsify the zero-paper claim if it found any published verification of a sonar-trained network, and finding a prior survey with substantive out-of-distribution or adversarial coverage would falsify the first-survey claim.

Watch

Extended reading notes

Core claim

The central claim is that robustness of sonar-based deep learning is not merely under-explored but nearly untouched: the survey finds zero papers on neural network verification of sonar models, and only single-digit counts in adversarial attacks, out-of-distribution detection, and uncertainty quantification. The paper also documents a concrete failure mode: a model trained on same-location side-scan sonar data can drop from 98% to 15% average precision when deployed under a different date and vehicle altitude, showing that domain shift is severe. It positions itself as the first comprehensive overview to map this landscape, comparing prior surveys, datasets, simulators, and robustness methods, and it proposes a workflow for verifying and hardening models before deployment.

Load-bearing premise

The paper's claim to be the first robustness-focused survey depends on its Table I judgement that every earlier sonar deep learning survey lacks substantive treatment of out-of-distribution detection, adversarial attacks, and uncertainty quantification.

Editorial extensions

If this is right

  • If the field adopts the proposed workflow, a sonar deep learning model would not be deployed until it passes out-of-distribution and uncertainty checks and either verification or adversarial testing, making safety a formal development step.
  • A shared community repository of open sonar datasets would allow different models to be compared on identical data, ending the current practice of comparing models trained on incompatible private datasets.
  • The documented sensitivity to sonar setup, including frequency, altitude, and colormap, implies that deployment documentation should record those parameters and that models should only be used inside a matching operating envelope.
  • Because neural network verification is completely empty in sonar, the first verifiable sonar model would open a new research direction rather than extend an existing one.
  • The paper's evidence that denoising alone cannot guarantee correct prediction redirects research attention from pre-processing toward intrinsic model robustness.

Reading between the lines

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

  • The 98%-to-15% precision collapse suggests that dataset shift is the dominant threat to sonar deep learning, so test-time adaptation methods developed for optical perception are a plausible next step that the paper does not explore.
  • The zero count for neural network verification likely reflects that available verifier tools target classification while most sonar tasks are detection and segmentation, making the adaptation of verifiers to one-stage detectors the highest-leverage test of the roadmap.
  • The proposed workflow could be turned into a benchmark: a standard sonar dataset plus prescribed perturbations such as black-line dropouts, altitude changes, and sonar brand changes would let the community quantify robustness improvements quantitatively.
  • The scarcity counts are a snapshot of the literature up to the paper's compilation, so the near-empty table is likely to fill quickly once the field's attention shifts toward reliability.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This manuscript is an accepted-version survey of sonar-based deep learning for underwater robotics, explicitly organized around robustness. It reviews perception tasks (classification, detection, segmentation, SLAM), catalogs 19 open-source sonar datasets and 8 open-source simulators, summarizes synthetic-data generation, and surveys robustness methods under four headings: neural network verification, adversarial attacks, out-of-distribution detection, and uncertainty quantification. It closes with a proposed pre/post-deployment robustness workflow and a public GitHub repository of sonar datasets. The paper's central positioning is that it is the first comprehensive robustness-focused sonar DL survey, supported by a comparative table of prior surveys (Table I) and by counts of existing robustness work (Section IV-G, Table IV).

Significance. If the novelty claim survives scrutiny, the paper would be a genuinely useful consolidated reference for AUV practitioners: it gathers a dispersed body of datasets, simulators, and robustness methods, identifies research gaps, and contributes an open repository and a concrete workflow for robustness evaluation. The absence of derivations means the paper's soundness rests on factual accuracy and completeness of coverage rather than on proof; on breadth it is strong, but the central 'first comprehensive' and scarcity claims are currently not independently checkable. The paper also gives credit where due to recent open-data efforts and to robustness works by several groups, including the authors' own, and it is generally clearly written.

major comments (3)
  1. [Section II, Table I and Section IV-G]
  2. [Section IV-D and Table IV]
  3. [Table IV and Section IV-D references]
minor comments (6)
  1. [Section III-D]
  2. [Table II and Section IV-A]
  3. [Section IV-A]
  4. [Section I-A and Section III-A]
  5. [Fig. 7 caption]
  6. [Section IV-B]

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation; the survey's 'first robustness overview' claim is a literature-classification claim, not a self-referential reduction.

full rationale

This is an overview paper, so there is no formal derivation chain whose output is equivalent to its inputs. The central claim to be the first sonar-based DL survey under the robustness scope is supported by Table I, which classifies eight prior surveys as not covering OOD, adversarial attacks, or uncertainty quantification, and by the scarcity counts in Table IV. Those classifications are audit claims about the literature; their validity is a question of search transparency and inclusion criteria (no database, query, or screening protocol is documented), not a circularity reduction. The authors' own works appear prominently: SWDD [5] and SubPipe [57] are among the datasets reviewed, and ROSAR [100] is one of four adversarial-attack papers counted in Table IV. These self-citations are not load-bearing in a circular sense: the dataset comparison would be unchanged if the authors' datasets were omitted, and dropping ROSAR would reduce the adversarial-attack count from four to three without altering the survey's conclusion that robustness research is scarce. No fitted parameter is relabeled as a prediction, and no prior result by the authors is invoked to forbid alternative framings. The only notable concern is that the 'first' and 'zero verification papers' statements rely on unstated search criteria, which is a reproducibility/correctness risk rather than a circularity. Accordingly, no circular step is exhibited; the score reflects the prominence of non-load-bearing self-citations, not a reduction.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

No free parameters or invented entities occur in this review. The only explicit axiom is the working definition of robustness, which shapes the selection and organization of the surveyed literature.

assumptions (1)
  • domain assumption Robustness is defined as the ability of AI systems to handle errors or inconsistencies during operation.
    The paper adopts this definition in Section I-B and uses it to frame the scope of the survey, determining which methods are included.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges." pith.science (2026). https://pith.science/paper/ZBM2MH5U

@misc{pith2026241211840,
  author       = {Pith},
  title        = {Pith review of: Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZBM2MH5U}},
  note         = {Machine review of arXiv:2412.11840}
}
read the original abstract

With the growing interest in underwater exploration and monitoring, Autonomous Underwater Vehicles (AUVs) have become essential. The recent interest in onboard Deep Learning (DL) has advanced real-time environmental interaction capabilities relying on efficient and accurate vision-based DL models. However, the predominant use of sonar in underwater environments, characterized by limited training data and inherent noise, poses challenges to model robustness. This autonomy improvement raises safety concerns for deploying such models during underwater operations, potentially leading to hazardous situations. This paper aims to provide the first comprehensive overview of sonar-based DL under the scope of robustness. It studies sonar-based DL perception task models, such as classification, object detection, segmentation, and SLAM. Furthermore, the paper systematizes sonar-based state-of-the-art datasets, simulators, and robustness methods such as neural network verification, out-of-distribution, and adversarial attacks. This paper highlights the lack of robustness in sonar-based DL research and suggests future research pathways, notably establishing a baseline sonar-based dataset and bridging the simulation-to-reality gap.

Figures

Figures reproduced from arXiv: 2412.11840 by the authors.

Figure 1
Figure 1. Sonar perception with Side Scan Sonar (SSS) [5] and Forward Looking [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Samples object detection on sonar images. Those two samples show [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Samples segmentation on sonar images. Those two samples show a [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Samples of Underwater Sonar Datasets. Those samples illustrate some of the SOA sonar datasets, which represent GeoTiff [88], SSS [89], FLS [90] [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Comparison between training and validation datasets [57]. Those [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Comparison between Side Scan Sonar with and without loss of [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Sonar-based Deep Learning - Robustness Workflow. This proposed [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Synthetic Enclosed Echoes: A New Dataset to Mitigate the Gap Between Simulated and Real-World Sonar Data

    cs.RO 2025-05 conditional novelty 5.0 of 10

    A new synthetic-plus-real sonar dataset for underwater 3D reconstruction, plus a regression variant of ElevateNET that outperforms prior methods in simulation.

Reference graph

Works this paper leans on

181 extracted references · 54 canonical work pages · cited by 1 Pith paper

  1. [156]

    Lambertian-based adversarial attacks on deep-learning-based underwater side-scan sonar image classification,

    Q. Ma, L. Jiang, and W. Yu, “Lambertian-based adversarial attacks on deep-learning-based underwater side-scan sonar image classification,” Pattern Recognition , vol. 138, p. 109363, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S003132032300064X

    work page 2023

  2. [1]

    Multi-auv motion planning for archeological site mapping and photogrammetric reconstruction,

    J. Wu, R. C. Bingham, S. Ting, K. Yager, Z. J. Wood, T. Gambin, and C. M. Clark, “Multi-auv motion planning for archeological site mapping and photogrammetric reconstruction,” Journal of Field Robotics, vol. 36, no. 7, pp. 1250–1269, 2019. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21905

    work page doi:10.1002/rob.21905 2019

  3. [2]

    Auv ”tuna-sand

    T. Nakatani et al., “Auv ”tuna-sand” and its exploration of hydrothermal vents at kagoshima bay,” in OCEANS 2008 - MTS/IEEE Kobe Techno- Ocean, Kobe, Japan, 2008, pp. 1–5

    2008

  4. [3]

    Multi-actuated auv body for windfarm inspection: Lessons from the bio-inspired robofish field trials,

    M. Wright, W. Gorma, Y . Luo, M. Post, Q. Xiao, and A. Durrant, “Multi-actuated auv body for windfarm inspection: Lessons from the bio-inspired robofish field trials,” in 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV), St. Johns, NL, Canada, 2020, pp. 1–6

    2020

  5. [4]

    The hugin 1000 autonomous underwater vehicle for military applications,

    P. E. Hagen, N. Storkersen, K. Vestgard, and P. Kartvedt, “The hugin 1000 autonomous underwater vehicle for military applications,” in Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492) , vol. 2, San Diego, CA, USA, 2003, pp. 1141–1145

    2003

  6. [5]

    Swdd: Sonar wall detection dataset,

    M. Aubard, L. Antal, M. Madureira, and E. ´Abrah´am, “Swdd: Sonar wall detection dataset,” Jan. 2024. [Online]. Available: https://doi.org/10.5281/zenodo.10528135

    work page doi:10.5281/zenodo.10528135 2024

  7. [6]

    Data augmentation using image translation for underwater sonar image segmentation,

    E.-h. Lee, B. Park, M.-H. Jeon, H. Jang, A. Kim, and S. Lee, “Data augmentation using image translation for underwater sonar image segmentation,” PLOS ONE, vol. 17, p. e0272602, 08 2022

    2022

  8. [7]

    Recent advances in ai for navigation and control of underwater robots,

    L. Christensen, J. de Gea Fern ´andez, M. Hildebrandt et al. , “Recent advances in ai for navigation and control of underwater robots,” Curr Robot Rep, vol. 3, pp. 165–175, 2022

    2022

Show all 181 references
  1. [8]

    Autonomous object identification in mine countermeasure missions,

    T. R. Krogstad, M. Wiig, P. Cleophas, and O. Midtgaard, “Autonomous object identification in mine countermeasure missions,” in OCEANS’11 MTS/IEEE KONA, Waikoloa, HI, USA, 2011, pp. 1–9

    2011

  2. [9]

    Archaeo- logic machine learning for shipwreck detection using lidar and sonar,

    L. Character, A. O. JR, T. Beach, and S. Luzzadder-Beach, “Archaeo- logic machine learning for shipwreck detection using lidar and sonar,” Remote Sens., vol. 13, no. 9, p. 1759, 2021

    2021

  3. [10]

    Lsts toolchain framework for deep learning implementation into autonomous underwater vehicle,

    M. Aubard, A. Madureira, L. Madureira, R. Campos, M. Costa, J. Pinto, and J. Sousa, “Lsts toolchain framework for deep learning implementation into autonomous underwater vehicle,” in OCEANS 2023 - Limerick , 2023, pp. 1–6. THIS IS THE ACCEPTED VERSION OF THE ARTICLE TO BE PUBL...

    2023

  4. [11]

    Mission planning and safety assessment for pipeline inspection using autonomous underwater vehicles: A framework based on behavior trees,

    M. Aubard, S. Quijano, O. ´Alvarez Tu ˜n´on, L. Antal, M. Costa, and Y . Brodskiy, “Mission planning and safety assessment for pipeline inspection using autonomous underwater vehicles: A framework based on behavior trees,” in OCEANS 2024 - Singapore , 2024, pp. 1–6

    2024

  5. [12]

    A.i. robustness: a human-centered perspective on technological challenges and opportunities,

    A. Tocchetti, L. Corti, A. Balayn, M. Yurrita, P. Lippmann, M. Bram- billa, and J. Yang, “A.i. robustness: a human-centered perspective on technological challenges and opportunities,” arXiv:2210.08906, 2022, https://arxiv.org/abs/2210.08906

    2022 arXiv

  6. [13]

    Safe ai for cps (invited paper),

    N. Fulton and A. Platzer, “Safe ai for cps (invited paper),” in 2018 IEEE International Test Conference (ITC) , Phoenix, AZ, USA, 2018, pp. 1–7

    2018

  7. [14]

    Opening the black box of deep neural networks via information,

    R. Shwartz-Ziv and N. Tishby, “Opening the black box of deep neural networks via information,” arXiv:1703.00810, 2017, https://arxiv.org/abs/1703.00810

    2017 arXiv

  8. [15]

    Ai challenges for society and ethics,

    J. Whittlestone and S. Clarke, “Ai challenges for society and ethics,” Oxford Handbooks Online , 2022

    2022

  9. [16]

    Explainable ai: Current status and future directions,

    P. Gohel, P. Singh, and M. Mohanty, “Explainable ai: Current status and future directions,” ArXiv, vol. arXiv:2107.07045, 2021

    2021 arXiv

  10. [17]

    Rethinking explainable machines: The gdprs “right to explanation

    B. Casey, A. Farhangi, and R. V ogl, “Rethinking explainable machines: The gdprs “right to explanation” debate and the rise of algorithmic audits in enterprise,” Berkeley Technology Law Journal, 2019

    2019

  11. [18]

    Explaining explanations: An overview of interpretability of machine learning,

    L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal, “Explaining explanations: An overview of interpretability of machine learning,” arXiv:1806.00069, 2019, https://arxiv.org/abs/1806.00069

    2019 arXiv

  12. [19]

    An introduction to underwater acoustics: Principles and applications,

    X. Lurton, “An introduction to underwater acoustics: Principles and applications,” 2010. [Online]. Available: https://api.semanticscholar. org/CorpusID:109354879

    2010

  13. [20]

    A review on deep learning-based approaches for automatic sonar target recognition,

    D. Neupane and J. Seok, “A review on deep learning-based approaches for automatic sonar target recognition,” Electronics, vol. 9, no. 11, 2020. [Online]. Available: https://www.mdpi.com/2079-9292/ 9/11/1972

    2020

  14. [21]

    Survey on deep learning based computer vision for sonar imagery,

    Y . Steiniger, D. Kraus, and T. Meisen, “Survey on deep learning based computer vision for sonar imagery,” Engineering Applications of Artificial Intelligence, vol. 114, p. 105157, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0952197622002718

    2022

  15. [22]

    Underwater target detection using deep learning: Methodologies, challenges, applications, and future evolution,

    A. Khan, M. Fouda, D.-T. Do, A. Almaleh, A. Alqahtani, and A. Rah- man, “Underwater target detection using deep learning: Methodologies, challenges, applications, and future evolution,” IEEE Access , vol. PP, pp. 1–1, 01 2024

    2024

  16. [23]

    Underwater target recognition methods based on the framework of deep learning: A survey,

    B. Teng and H. Zhao, “Underwater target recognition methods based on the framework of deep learning: A survey,” International Journal of Advanced Robotic Systems , vol. 17, no. 6, p. 1729881420976307,

  17. [24]

    A review on the wavelet methods for sonar image segmentation,

    Y . Tian, L. Lan, and H. Guo, “A review on the wavelet methods for sonar image segmentation,” International Journal of Advanced Robotic Systems, vol. 17, no. 4, p. 1729881420936091, 2020. [Online]. Available: https://doi.org/10.1177/1729881420936091

    2020 doi

  18. [25]

    Acoustic fish species identification using deep learning and machine learning algorithms: A systematic review,

    A. Yassir, S. Jai Andaloussi, O. Ouchetto, K. Mamza, and M. Serghini, “Acoustic fish species identification using deep learning and machine learning algorithms: A systematic review,” Fisheries Research , vol. 266, p. 106790, 2023. [Online]. Available: https://www.sciencedirect...

    2023

  19. [26]

    Deep learning algorithms for sonar imagery analysis and its application in aquaculture: A review,

    Y . Chai, H. Yu, L. Xu, D. Li, and Y . Chen, “Deep learning algorithms for sonar imagery analysis and its application in aquaculture: A review,” IEEE Sensors Journal , vol. 23, no. 23, pp. 28 549–28 563, 2023

    2023

  20. [27]

    A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance,

    L. C. F. Domingos, P. E. Santos, P. S. M. Skelton, R. S. A. Brinkworth, and K. Sammut, “A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance,” Sensors, vol. 22, no. 6, 2022. [Online]. Available: https://www.mdpi.com/1424-82...

    2022

  21. [28]

    Imagenet classification with deep convolutional neural networks,

    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems , F. Pereira, C. Burges, L. Bottou, and K. Weinberger, Eds., vol. 25. Curran Associates, Inc., 2012. [Online]....

    2012

  22. [29]

    Gradient-based learning applied to document recognition,

    Y . Lecun, L. Bottou, Y . Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998

    1998

  23. [30]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2015

    2015

  24. [31]

    Rich feature hierarchies for accurate object detection and semantic segmentation,

    R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” 2014

    2014

  25. [32]

    Fast r-cnn,

    R. Girshick, “Fast r-cnn,” in 2015 IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 1440–1448

    2015

  26. [33]

    Faster r-cnn: Towards real- time object detection with region proposal networks,

    S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real- time object detection with region proposal networks,” ArXiv, 2015, https://arxiv.org/abs/1506.01497

    2015 arXiv

  27. [34]

    You only look once: Unified, real-time object detection,

    J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” ArXiv, 2015, https://arxiv.org/abs/1506.02640

    2015 arXiv

  28. [35]

    Ssd: Single shot multibox detector,

    W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” ArXiv, 2015, https://doi.org/10.1007/978-3-319-46448-0 2

    2015 doi

  29. [36]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,”ArXiv, 2017, https://arxiv.org/abs/1706.03762

    2017 arXiv

  30. [37]

    Serial order: A parallel distributed processing approach,

    M. I. Jordan, “Serial order: A parallel distributed processing approach,” in Advances in psychology . Elsevier, 1997, vol. 121, pp. 471–495

    1997

  31. [38]

    Long short-term memory,

    S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, p. 1735–1780, 1997

    1997

  32. [39]

    End-to-end object detection with transformers,

    N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” ArXiv, 2020, https://arxiv.org/abs/2005.12872

    2020 arXiv

  33. [40]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” ArXiv, 2020, https://arxiv.org/abs/2010.11929

    2020 arXiv

  34. [41]

    Rotated object detection with forward-looking sonar in underwater applications,

    G. Neves, M. Ruiz, J. Fontinele, and L. Oliveira, “Rotated object detection with forward-looking sonar in underwater applications,” Expert Systems with Applications , vol. 140, p. 112870, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S0957417419305809

    2020

  35. [42]

    Real-time underwater maritime object detection in side-scan sonar images based on transformer-yolov5,

    Y . Yu, J. Zhao, Q. Gong, C. Huang, G. Zheng, and J. Ma, “Real-time underwater maritime object detection in side-scan sonar images based on transformer-yolov5,” Remote Sensing, vol. 13, no. 18, p. 3555, 2021

    2021

  36. [43]

    Real-time automatic wall detection and localization based on side scan sonar images,

    M. Aubard, A. Madureira, L. Madureira, and J. Pinto, “Real-time automatic wall detection and localization based on side scan sonar images,” in 2022 IEEE/OES Autonomous Underwater Vehicles Sympo- sium (AUV), 2022, pp. 1–6

    2022

  37. [44]

    Yolox: Exceeding yolo series in 2021,

    Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “Yolox: Exceeding yolo series in 2021,” ArXiv, 2021, https://arxiv.org/abs/2107.08430

    2021 arXiv

  38. [45]

    Knowledge distillation in YOLOX-ViT for side-scan sonar object detection,

    M. Aubard, L. Antal, A. Madureira, and E. ´Abrah´am, “Knowledge distillation in YOLOX-ViT for side-scan sonar object detection,” CoRR, vol. abs/2403.09313, 2024. [Online]. Available: https://doi.org/ 10.48550/arXiv.2403.09313

    2024 doi arXiv

  39. [46]

    Fully convolutional networks for semantic segmentation,

    J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” 2015

    2015

  40. [47]

    U-net: Convolutional net- works for biomedical image segmentation,

    O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional net- works for biomedical image segmentation,” 2015

    2015

  41. [48]

    Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

    L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” 2017

    2017

  42. [49]

    Pyramid scene parsing network,

    H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” 2017

    2017

  43. [50]

    A submarine pipeline segmentation method for noisy forward-looking sonar images using global information and coarse segmentation,

    W. Chen, Z. Liu, H. Zhang, M. Chen, and Y . Zhang, “A submarine pipeline segmentation method for noisy forward-looking sonar images using global information and coarse segmentation,” Applied Ocean Research , vol. 112, p. 102691, 2021. [Online]. Available: https://www.sciencedi...

    2021

  44. [51]

    Side-scan sonar images segmentation for auv with recurrent residual convolutional neural network module and self-guidance module,

    F. Yu, B. He, K. Li, T. Yan, Y . Shen, Q. Wang, and M. Wu, “Side-scan sonar images segmentation for auv with recurrent residual convolutional neural network module and self-guidance module,” Applied Ocean Research , vol. 113, p. 102608, 2021. [Online]. Available: https://www.s...

    2021

  45. [52]

    Attention u-net: Learning where to look for the pancreas,

    O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y . Hammerla, B. Kainz, B. Glocker, and D. Rueckert, “Attention u-net: Learning where to look for the pancreas,” 2018

    2018

  46. [53]

    Rethinking semantic segmenta- tion from a sequence-to-sequence perspective with transformers,

    S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y . Wang, Y . Fu, J. Feng, T. Xiang, P. H. S. Torr, and L. Zhang, “Rethinking semantic segmenta- tion from a sequence-to-sequence perspective with transformers,” 2021

    2021

  47. [54]

    Segmenter: Trans- former for semantic segmentation,

    R. Strudel, R. Garcia, I. Laptev, and C. Schmid, “Segmenter: Trans- former for semantic segmentation,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . Los Alamitos, CA, USA: IEEE Computer Society, oct 2021, pp. 7242–7252. [Online]. Available: https://doi....

    2021

  48. [55]

    Mitu-net: An efficient mix transformer u-like network for forward-looking sonar image segmentation,

    Y . Liang, X. Zhu, and J. Zhang, “Mitu-net: An efficient mix transformer u-like network for forward-looking sonar image segmentation,” in 2022 THIS IS THE ACCEPTED VERSION OF THE ARTICLE TO BE PUBLISHED IN THE IEEE JOURNAL OF OCEANIC ENGINEERING. 17 IEEE 2nd International Conf...

    2022

  49. [56]

    Sonarnet: Hybrid cnn-transformer- hog framework and multifeature fusion mechanism for forward-looking sonar image segmentation,

    J. He, J. Chen, H. Xu, and Y . Yu, “Sonarnet: Hybrid cnn-transformer- hog framework and multifeature fusion mechanism for forward-looking sonar image segmentation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–17, 2024

    2024

  50. [57]

    Subpipe: A submarine pipeline inspection dataset for segmentation and visual-inertial localization,

    O. ´Alvarez Tu ˜n´on, L. R. Marnet, M. Aubard, L. Antal, M. Costa, and Y . Brodskiy, “Subpipe: A submarine pipeline inspection dataset for segmentation and visual-inertial localization,” in OCEANS 2024 - Singapore, 2024, pp. 1–7

    2024

  51. [58]

    Semantic segmentation of underwater imagery: Dataset and benchmark,

    M. J. Islam et al. , “Semantic segmentation of underwater imagery: Dataset and benchmark,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Las Vegas, NV , USA, 2020, pp. 1769–1776

    2020

  52. [59]

    Simultaneous map building and localization for an autonomous mobile robot,

    J. Leonard and H. Durrant-whyte, “Simultaneous map building and localization for an autonomous mobile robot,” in Intelligent Robots and Systems’ 91. ’Intelligence for Mechanical Systems, Proceedings IROS’91. IEEE/RSJ International Workshop on , 1991, p. 1442–1447

    1991

  53. [60]

    A new approach to linear filtering and prediction problems,

    R. Emil, “A new approach to linear filtering and prediction problems,” Transactions of the ASME–Journal of Basic Engineering , 1960

    1960

  54. [61]

    A tutorial on graph-based slam,

    G. Grisetti, R. K ¨ummerle, C. Stachniss, and W. Burgard, “A tutorial on graph-based slam,” IEEE Transactions on Intelligent Transportation Systems Magazine, vol. 2, pp. 31–43, 2010

    2010

  55. [62]

    Feature-based under- water localization using imaging sonar in confined environments,

    A. J. Oliveira, B. M. Ferreira, and N. A. Cruz, “Feature-based under- water localization using imaging sonar in confined environments,” in OCEANS 2021: San Diego - Porto , 2021, pp. 1–7

    2021

  56. [63]

    A fully-automatic side-scan sonar simultaneous localization and mapping framework,

    J. Zhang, Y . Xie, L. Ling, and J. Folkesson, “A fully-automatic side-scan sonar simultaneous localization and mapping framework,” IET Radar, Sonar & Navigation , Nov. 2023. [Online]. Available: http://dx.doi.org/10.1049/rsn2.12500

    2023 doi

  57. [64]

    Toward geometric deep slam,

    D. DeTone, T. Malisiewicz, and A. Rabinovich, “Toward geometric deep slam,” arXiv preprint arXiv:1707.07410 , 2017

    2017 arXiv

  58. [65]

    Review of underwater slam techniques,

    F. Hidalgo and T. Br ¨aunl, “Review of underwater slam techniques,” in 2015 6th International Conference on Automation, Robotics and Applications (ICARA), Queenstown, New Zealand, 2015, pp. 306–311

    2015

  59. [66]

    Visual slam for underwater vehicles: A survey,

    S. Zhang, S. Zhao, D. An, J. Liu, H. Wang, Y . Feng, D. Li, and R. Zhao, “Visual slam for underwater vehicles: A survey,” Computer Science Review, vol. 46, p. 100510, 2022

    2022

  60. [67]

    Svin2: A multi-sensor fusion-based underwater slam system,

    S. RAHMAN, A. Q. LI, and I. REKLEITIS, “Svin2: A multi-sensor fusion-based underwater slam system,” The International Journal of Robotics Research, vol. 41, no. 11-12, pp. 1022–1042, 2022

    2022

  61. [68]

    Svin2: An underwater slam system using sonar, visual, inertial, and depth sensor,

    ——, “Svin2: An underwater slam system using sonar, visual, inertial, and depth sensor,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019, pp. 1861–1868

    2019

  62. [69]

    Adaptive tuning of a kalman filter via fuzzy logic for an intelligent auv navigation system,

    D. Loebis, R. Sutton, J. Chudley, and W. Naeem, “Adaptive tuning of a kalman filter via fuzzy logic for an intelligent auv navigation system,” Control Engineering Practice , vol. 12, no. 12, pp. 1531–1539, 2004, guidance and control of underwater vehicles. [Online]. Available:...

    2004

  63. [70]

    A dense subframe-based slam framework with side-scan sonar,

    J. Zhang, Y . Xie, L. Ling, and J. Folkesson, “A dense subframe-based slam framework with side-scan sonar,” 2023

    2023

  64. [71]

    Savor: Sonar-aided visual odometry and reconstruction for autonomous underwater vehicles,

    J. Coffelt, P. Kampmann, and B. Wehbe, “Savor: Sonar-aided visual odometry and reconstruction for autonomous underwater vehicles,” 10 2024

    2024

  65. [72]

    An overview of key slam technologies for underwater scenes,

    X. Wang, X. Fan, P. Shi, J. Ni, and Z. Zhou, “An overview of key slam technologies for underwater scenes,” Remote Sensing , vol. 15, no. 10, 2023. [Online]. Available: https://www.mdpi.com/2072-4292/ 15/10/2496

    2023

  66. [73]

    Autonomous underwater vehicle navigation using sonar image matching based on convolutional neural network,

    W. Yang, S. Fan, S. Xu, P. King, B. Kang, and E. Kim, “Autonomous underwater vehicle navigation using sonar image matching based on convolutional neural network,” IFAC-PapersOnLine, vol. 52, no. 21, pp. 156–162, 2019, 12th IFAC Conference on Control Applications in Marine Syst...

    2019

  67. [74]

    Diverse ocean noise classifi- cation using deep learning,

    B. Mishachandar and S. Vairamuthu, “Diverse ocean noise classifi- cation using deep learning,” Applied Acoustics , vol. 181, p. 108141, 2021

    2021

  68. [75]

    A dataset with multibeam forward- looking sonar for underwater object detection,

    K. Xie, J. Yang, and K. Qiu, “A dataset with multibeam forward- looking sonar for underwater object detection,” Scientific Data, vol. 9, p. 739, 12 2022

    2022

  69. [76]

    An adaptive denoising and detection approach for underwater sonar image,

    X. Wang, Q. Li, J. Yin, X. Han, and W. Hao, “An adaptive denoising and detection approach for underwater sonar image,” Remote Sensing , vol. 11, no. 4, 2019. [Online]. Available: https: //www.mdpi.com/2072-4292/11/4/396

    2019

  70. [77]

    Sonar image denoising based on anisotropic guided filtering,

    C. Wang, L. Shen, Y . Fan, T. Chen, and X. Tan, “Sonar image denoising based on anisotropic guided filtering,” in 2022 5th International Con- ference on Intelligent Autonomous Systems (ICoIAS) , 2022, pp. 54–59

    2022

  71. [78]

    Deep denoising method for side scan sonar images without high-quality reference data,

    X. Zhou, C. Yu, X. Yuan, and C. Luo, “Deep denoising method for side scan sonar images without high-quality reference data,” in 2022 2nd International Conference on Computer, Control and Robotics (ICCCR), 2022, pp. 241–245

    2022

  72. [79]

    Remove and recover: two stage convolutional autoencoder based sonar image enhancement algorithm,

    T. Liu, S. Yan, and G. Wang, “Remove and recover: two stage convolutional autoencoder based sonar image enhancement algorithm,” Multimedia Tools and Applications , 2023. [Online]. Available: https://doi.org/10.1007/s11042-023-17673-z

    2023 doi

  73. [80]

    Research on a feature enhancement extraction method for underwater targets based on deep autoencoder networks,

    F. Ji, G. Li, S. Lu, and J. Ni, “Research on a feature enhancement extraction method for underwater targets based on deep autoencoder networks,” Applied Sciences, vol. 14, no. 4, 2024. [Online]. Available: https://www.mdpi.com/2076-3417/14/4/1341

    2024

  74. [81]

    Connectionist Bench (Sonar, Mines vs. Rocks),

    T. Sejnowski and R. Gorman, “Connectionist Bench (Sonar, Mines vs. Rocks),” UCI Machine Learning Repository, DOI: https://doi.org/10.24432/C5T01Q

    doi

  75. [82]

    Side-scan sonar and wa- ter profiler data from Lago Grey,

    S. Sugiyama, M. Minowa, and M. Schaefer, “Side-scan sonar and wa- ter profiler data from Lago Grey,” https://data.mendeley.com/datasets/ jpz52pm9sc/1, 2019

    2019

  76. [83]

    Underwater ice terrace observed at the front of glaciar grey, a freshwater calving glacier in patagonia,

    ——, “Underwater ice terrace observed at the front of glaciar grey, a freshwater calving glacier in patagonia,” Geophysical Research Letters, vol. 46, no. 5, pp. 2602–2609, 2019. [Online]. Available: https: //agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018GL081441

    2019 doi

  77. [84]

    Ireland’s Open Data Portal,

    Government of Ireland, “Ireland’s Open Data Portal,” accessed: [07/02/2024]. [Online]. Available: https://data.gov.ie/dataset?q=sonar& sort=title string+desc

    2024

  78. [85]

    U.S. Government’s Open Data,

    Government of USA, “U.S. Government’s Open Data,” accessed: [07/02/2024]. [Online]. Available: https://data.gov/

    2024

  79. [86]

    Side-scan sonar images of marine engineering geology (Marine PULSE Dataset),

    X. Du, “Side-scan sonar images of marine engineering geology (Marine PULSE Dataset),” May 2023. [Online]. Available: https: //doi.org/10.5281/zenodo.7922705

    2023 doi

  80. [87]

    Revealing the potential of deep learning for detecting submarine pipelines in side-scan sonar images: An investigation of pre-training datasets,

    X. Du, Y . Sun, Y . Song, L. Dong, and X. Zhao, “Revealing the potential of deep learning for detecting submarine pipelines in side-scan sonar images: An investigation of pre-training datasets,” Remote Sensing , vol. 15, no. 19, 2023. [Online]. Available: https://www.mdpi.com/...

    2023

  81. [88]

    Side Scan Sonar mosaics of selected northern Adriatic mesophotic biogenic reefs off Venice (GeoTIFF image),

    E. Andreoli, F. Boscolo, A. Carlin, D. Curiel, E. Gordini, L. Mizzan, E. Molin, M. Ombrelli, G. Pessa, A. Rismondo, S. Rizzardi, S. Vanin, and M. Zanetto, “Side Scan Sonar mosaics of selected northern Adriatic mesophotic biogenic reefs off Venice (GeoTIFF image),” Mar

  82. [89]

    Machine learning for shipwreck segmentation from side scan sonar imagery: Dataset and benchmark,

    A. V . Sethuraman, A. Sheppard, O. Bagoren, C. Pinnow, J. Anderson, T. C. Havens, and K. A. Skinner, “Machine learning for shipwreck segmentation from side scan sonar imagery: Dataset and benchmark,” 2024

    2024

  83. [90]

    An acous- tic and optical dataset for the perception of underwater unexploded ordnance (uxo),

    N. Dahn, M. Bande, P. Sharma, L. Christensen, O. Geisler, J. Mohrmann, T. Frey, K. Sanghamreddy, and F. Kirchner, “An acous- tic and optical dataset for the perception of underwater unexploded ordnance (uxo),” 09 2024

    2024

  84. [91]

    A Large Scale Side-Scan Sonar Dataset of Seafloor Sediments for Self-Supervised Pretraining,

    F. D. B. Martinez-Clavel Valles, H. H. Rajani, N. Gracias, and R. Garcia, “A Large Scale Side-Scan Sonar Dataset of Seafloor Sediments for Self-Supervised Pretraining,” Dec. 2023. [Online]. Available: https://doi.org/10.5281/zenodo.10209445

    2023 doi

  85. [92]

    Side-scan sonar imaging for Mine detec- tion,

    N. P. Santos and R. MOURA, “Side-scan sonar imaging for Mine detec- tion,” 1 2024. [Online]. Available: https://figshare.com/articles/dataset/ i Side-scan sonar imaging for Mine detection i /24574879

    2024

  86. [93]

    DIDSON fish data and code for analysis,

    M. Ogburn, J. Lefcheck, and J. Olson, “DIDSON fish data and code for analysis,” 1 2023. [Online]. Avail- able: https://smithsonian.figshare.com/articles/dataset/DIDSON fish data and code for analysis/19611510

    2023

  87. [94]

    Optimising the workflow for fish detection in didson (dual-frequency identification sonar) data with the use of optical flow and a genetic algorithm,

    T.-M. Perivolioti, M. Tu ˇser, D. Terzopoulos, S. P. Sgardelis, and I. Antoniou, “Optimising the workflow for fish detection in didson (dual-frequency identification sonar) data with the use of optical flow and a genetic algorithm,” Water, vol. 13, no. 9, 2021. [Online]. Avail...

    2021

  88. [95]

    Marine Debris Turntable,

    “Marine Debris Turntable,” https://github.com/mvaldenegro/ marine-debris-fls-datasets, 2024, accessed: [27/02/2024]

    2024

  89. [96]

    The marine debris dataset for forward-looking sonar semantic segmentation,

    D. Singh and M. Valdenegro-Toro, “The marine debris dataset for forward-looking sonar semantic segmentation,” 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) , pp. 3734–3742, 2021. [Online]. Available: https://api.semanticscholar. org/CorpusID:237091259

    2021

  90. [97]

    Synthetic aperture sonar seabed environment dataset (sassed),

    J. T. Cobb, “Synthetic aperture sonar seabed environment dataset (sassed),” 2022. [Online]. Available: https://doi.org/10.17632/ s5j5gzr2vc.4 THIS IS THE ACCEPTED VERSION OF THE ARTICLE TO BE PUBLISHED IN THE IEEE JOURNAL OF OCEANIC ENGINEERING. 18

    2022

  91. [98]

    “NKSID,” https://github.com/Jorwnpay/Sonar-OLTR, 2024, accessed: [20/03/2024]

    2024

  92. [99]

    Open-set recognition with long-tail sonar images,

    W. Jiao, J. Zhang, and C. Zhang, “Open-set recognition with long-tail sonar images,” Expert Systems with Applications , vol. 249, p. 123495, 2024. [Online]. Available: https://www.sciencedirect.com/ science/article/pii/S0957417424003609

    2024

  93. [100]

    Rosar: An adversarial re-training framework for robust side- scan sonar object detection,

    M. Aubard, L. Antal, A. Madureira, L. F. Teixeira, and E. ´Abrah´am, “Rosar: An adversarial re-training framework for robust side- scan sonar object detection,” 2024. [Online]. Available: https: //arxiv.org/abs/2410.10554

    2024 arXiv

  94. [101]

    Aurora, a multi sensor dataset for robotic ocean exploration,

    M. Bernardi, B. Hosking, C. Petrioli, B. J. Bett, D. Jones, V . A. Huvenne, R. Marlow, M. Furlong, S. McPhail, and A. Munafo, “Aurora, a multi sensor dataset for robotic ocean exploration,” 2020. [Online]. Available: https://dx.doi.org/10.21227/nnms-te61

    2020 doi

  95. [102]

    A bathymetric mapping and slam dataset with high-precision ground truth for marine robotics,

    K. Krasnosky, C. Roman, and D. Casagrande, “A bathymetric mapping and slam dataset with high-precision ground truth for marine robotics,” The International Journal of Robotics Research , vol. 41, no. 1, pp. 12–19, 2022. [Online]. Available: https: //doi.org/10.1177/02783649211044749

    2022 doi

  96. [103]

    Underwater caves sonar data set,

    A. Mallios, E. Vidal, R. Campos, and M. Carreras, “Underwater caves sonar data set,” The International Journal of Robotics Research, vol. 36, pp. 1247 – 1251, 2017. [Online]. Available: https://api.semanticscholar.org/CorpusID:39662325

    2017

  97. [105]

    Microsoft coco: Common objects in context,

    T.-Y . Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll ´ar, and C. L. Zitnick, “Microsoft coco: Common objects in context,” European conference on computer vision, pp. 740–755, 2014

    2014

  98. [106]

    Imagenet: A large-scale hierarchical image database,

    J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition. IEEE, 2009, pp. 248–255

    2009

  99. [107]

    An open source tool for simulation and supervision of underwater intervention missions,

    M. Prats, J. P ´erez Soler, J. Fernandez, and P. Sanz, “An open source tool for simulation and supervision of underwater intervention missions,” 10 2012, pp. 2577–2582

    2012

  100. [108]

    Development of a side scan sonar module for the underwater simula- tor,

    D.-H. Gwon, J. Kim, M. H. Kim, H. G. Park, T. Y . Kim, and A. Kim, “Development of a side scan sonar module for the underwater simula- tor,” in 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) , 2017, pp. 662–665

    2017

  101. [109]

    Uuv simulator: A gazebo-based package for un- derwater intervention and multi-robot simulation,

    M. M. M. Manh ˜aes, S. A. Scherer, M. V oss, L. R. Douat, and T. Rauschenbach, “Uuv simulator: A gazebo-based package for un- derwater intervention and multi-robot simulation,” in OCEANS 2016 MTS/IEEE Monterey, 2016, pp. 1–8

    2016

  102. [110]

    A novel gpu-based sonar simulator for real-time applications,

    R. Cerqueira, T. Trocoli, G. Neves, S. Joyeux, J. Albiez, and L. Oliveira, “A novel gpu-based sonar simulator for real-time applications,” Computers & Graphics , vol. 68, no. Supplement C, pp. 66–76, 2017. [Online]. Available: http://www.sciencedirect.com/ science/article/pii/...

    2017

  103. [111]

    Dave aquatic virtual environment: Toward a general underwater robotics simula- tor,

    M. M. Zhang, W.-S. Choi, J. Herman, D. Davis, C. V ogt, M. McCarrin, Y . Vijay, D. Dutia, W. Lew, S. Peters, and B. Bingham, “Dave aquatic virtual environment: Toward a general underwater robotics simula- tor,” in 2022 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV), 2...

    2022

  104. [112]

    Stonefish: An advanced open-source simulation tool de- signed for marine robotics, with a ros interface,

    P. Cie ´slak, “Stonefish: An advanced open-source simulation tool de- signed for marine robotics, with a ros interface,” in OCEANS 2019 - Marseille, 2019, pp. 1–6

    2019

  105. [113]

    Unav-sim: A visually realistic under- water robotics simulator and synthetic data-generation framework,

    A. Abdelhakim, O. ´Alvarez Tu˜n´on, H. I. Ugurlu, J. le Fevre Sejersen, Y . Brodskiy, and E. Kayacan, “Unav-sim: A visually realistic under- water robotics simulator and synthetic data-generation framework,” in 2023 International Conference on Advanced Robotics (ICAR) , 2023

    2023

  106. [114]

    Marus - a marine robotics simulator,

    I. Loncar, J. Obradovic, N. Krasevac, L. Mandic, I. Kvasic, F. Ferreira, V . Slosic, D. Nad, and N. Miskovic, “Marus - a marine robotics simulator,” in OCEANS 2022, Hampton Roads , 2022, pp. 1–7

    2022

  107. [115]

    Holoocean: An underwater robotics simulator,

    E. Potokar, S. Ashford, M. Kaess, and J. G. Mangelson, “Holoocean: An underwater robotics simulator,” in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 3040–3046

    2022

  108. [116]

    Holoocean: Realistic sonar simulation,

    E. Potokar, K. Lay, K. Norman, D. Benham, T. B. Neilsen, M. Kaess, and J. G. Mangelson, “Holoocean: Realistic sonar simulation,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 8450–8456

    2022

  109. [117]

    Domain randomization for transferring deep neural networks from simulation to the real world,

    J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” 2017

    2017

  110. [118]

    Ros: an open-source robot operating system,

    M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Ng, “Ros: an open-source robot operating system,” vol. 3, 01 2009

    2009

  111. [119]

    Airsim: High-fidelity visual and physical simulation for autonomous vehicles,

    S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” 2017

    2017

  112. [120]

    The rock-gazebo integration and a real-time auv simulation,

    T. Watanabe, G. Neves, R. Cerqueira, T. Trocoli, M. Reis, S. Joyeux, and J. Albiez, “The rock-gazebo integration and a real-time auv simulation,” in 2015 12th Latin American Robotics Symposium and 2015 3rd Brazilian Symposium on Robotics (LARS-SBR) , 2015, pp. 132–138

    2015

  113. [121]

    R. Rost, J. Kessenich, B. Lichtenbelt, H. Malan, and M. Weiblen, OpenGL Shading Language , 01 2006

    2006

  114. [122]

    Underwater simulators analysis for digital twinning,

    N. Ciuccoli, L. Screpanti, and D. Scaradozzi, “Underwater simulators analysis for digital twinning,” IEEE Access, vol. 12, pp. 34 306–34 324, 2024

    2024

  115. [123]

    MASTODON,

    “MASTODON,” https://github.com/Sonar-Sim/MASTODON, 2024, accessed: [25/11/2024]

    2024

  116. [124]

    An overview of the mastodon sonar simulator,

    D. Woods, “An overview of the mastodon sonar simulator,” The Journal of the Acoustical Society of America , vol. 154, no. 4 supplement, pp. A211–A211, 10 2023. [Online]. Available: https://doi.org/10.1121/10.0023310

    2023 doi

  117. [125]

    Gpu-based simula- tion of side-looking sonar images,

    E. Coiras, A. Ramirez-Montesinos, and J. Groen, “Gpu-based simula- tion of side-looking sonar images,” in OCEANS 2009-EUROPE, 2009, pp. 1–6

    2009

  118. [126]

    Realistic sonar image simulation using deep learning for underwater object detection,

    M. Sung, J. Kim, M. Lee, B. Kim, T. Kim, J. Kim, and S.-C. Yu, “Realistic sonar image simulation using deep learning for underwater object detection,” International Journal of Control, Automation and Systems, vol. 18, pp. 1–12, 02 2020

    2020

  119. [127]

    Stars: Zero-shot sim-to-real transfer for segmentation of shipwrecks in sonar imagery,

    A. V . Sethuraman and K. A. Skinner, “Stars: Zero-shot sim-to-real transfer for segmentation of shipwrecks in sonar imagery,” arXiv preprint arXiv:2310.01667, 2023

    2023 arXiv

  120. [128]

    Generative adversarial net- works,

    I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y . Bengio, “Generative adversarial net- works,” Commun. ACM, vol. 63, no. 11, p. 139–144, 2020

    2020

  121. [129]

    Adversarial training methods for semi-supervised text classification,

    T. Miyato, A. Dai, and I. Goodfellow, “Adversarial training methods for semi-supervised text classification,” arXiv preprint arXiv:1605.07725 , 2016

    2016 arXiv

  122. [130]

    Enhancing underwater imagery using generative adversarial networks,

    C. Fabbri, M. J. Islam, and J. Sattar, “Enhancing underwater imagery using generative adversarial networks,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 7159–7165

    2018

  123. [131]

    Sonar2depth: Acoustic-based 3d reconstruction using cgans,

    N. Jaber, B. Wehbe, and F. Kirchner, “Sonar2depth: Acoustic-based 3d reconstruction using cgans,” in 2023 IEEE/RSJ International Confer- ence on Intelligent Robots and Systems (IROS) , 2023, pp. 5828–5835

    2023

  124. [132]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 6840–6851. [Online]. Available: htt...

    2020

  125. [133]

    A side-scan sonar image synthesis method based on a diffusion model,

    Y . Zhiwei, J. Zhao, H. Zhang, Y . Yu, and C. Huang, “A side-scan sonar image synthesis method based on a diffusion model,” Journal of Marine Science and Engineering , vol. 11, p. 1103, 05 2023

    2023

  126. [134]

    Side-scan sonar image classification with zero-shot and style transfer,

    Z. Bai, H. Xu, Q. Ding, and X. Zhang, “Side-scan sonar image classification with zero-shot and style transfer,” IEEE Transactions on Instrumentation and Measurement , vol. 73, pp. 1–15, 2024

    2024

  127. [135]

    Underwater image classifica- tion using deep convolutional neural networks and data augmentation,

    Y . Xu, Y . Zhang, H. Wang, and X. Liu, “Underwater image classifica- tion using deep convolutional neural networks and data augmentation,” in 2017 IEEE International Conference on Signal Processing, Commu- nications and Computing (ICSPCC) , 2017, pp. 1–5

    2017

  128. [136]

    Underwater object classification in sidescan sonar images using deep transfer learning and semisynthetic training data,

    G. Huo, Z. Wu, and J. Li, “Underwater object classification in sidescan sonar images using deep transfer learning and semisynthetic training data,” IEEE Access, vol. 8, pp. 47 407–47 418, 2020

    2020

  129. [137]

    Introduction to neural network verification,

    A. Albarghouthi, “Introduction to neural network verification,” Foundations and Trends® in Programming Languages , vol. 7, no. 1–2, pp. 1–157, 2021. [Online]. Available: http://dx.doi.org/10.1561/ 2500000051

    2021

  130. [138]

    A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability,

    X. Huang, D. Kroening, W. Ruan, J. Sharp, Y . Sun, E. Thamo, M. Wu, and X. Yi, “A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability,” 2020

    2020

  131. [139]

    Beta- crown: Efficient bound propagation with per-neuron split constraints for complete and incomplete neural network verification,

    S. Zhang, T.-W. Weng, P.-Y . Chen, C.-J. Hsieh, and L. Daniel, “Beta- crown: Efficient bound propagation with per-neuron split constraints for complete and incomplete neural network verification,” in Advances in Neural Information Processing Systems , 2021

    2021

  132. [140]

    An abstract domain for certifying neural networks,

    G. Singh, T. Gehr, M. P ¨uschel, and M. Vechev, “An abstract domain for certifying neural networks,” in Proceedings of the ACM on Pro- gramming Languages, vol. 3, no. POPL. ACM, 2019, p. 41

    2019

  133. [141]

    An abstract domain for certifying neural networks,

    G. Singh, T. Gehr, M. P ¨uschel, and M. Vechev, “An abstract domain for certifying neural networks,” Proc. ACM Program. Lang., vol. 3, no. POPL, Jan. 2019. [Online]. Available: https://doi.org/10.1145/3290354 THIS IS THE ACCEPTED VERSION OF THE ARTICLE TO BE PUBLISHED IN THE I...

    2019 doi

  134. [142]

    Reluplex: An efficient smt solver for verifying deep neural networks,

    G. Katz, C. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer, “Reluplex: An efficient smt solver for verifying deep neural networks,” in International Conference on Computer Aided Verification. Springer, 2017, pp. 97–117

    2017

  135. [143]

    G. Katz, D. Huang, D. Ibeling, K. Julian, C. Lazarus, R. Lim, P. Shah, S. Thakoor, H. Wu, A. Zelji´c, D. Dill, M. Kochenderfer, and C. Barrett, The Marabou Framework for Verification and Analysis of Deep Neural Networks, 07 2019, pp. 443–452

    2019

  136. [144]

    Toward evaluating the robustness of deep learning based rain removal algorithm in autonomous driving,

    Y . Qin, J. Hu, and B. Wu, “Toward evaluating the robustness of deep learning based rain removal algorithm in autonomous driving,” in Proceedings of the 2023 Secure and Trustworthy Deep Learning Systems Workshop , ser. SecTL ’23. New York, NY , USA: Association for Computing M...

    2023

  137. [145]

    Explaining and harnessing adversarial examples,

    I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” CoRR, vol. abs/1412.6572, 2014. [Online]. Available: https://api.semanticscholar.org/CorpusID:6706414

    2014 arXiv

  138. [146]

    Towards deep learning models resistant to adversarial attacks,

    A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” 2019

    2019

  139. [147]

    Deepfool: a simple and accurate method to fool deep neural networks,

    S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” 2016

    2016

  140. [148]

    The limitations of deep learning in adversarial settings,

    N. Papernot, P. Mcdaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” 2016 IEEE European Symposium on Security and Privacy (EuroS&P) , pp. 372–387, 2015. [Online]. Available: https: //api.semanticscholar.org/Cor...

    2016

  141. [149]

    Transferability in machine learning: from phenomena to black-box attacks using adversarial samples,

    N. Papernot, P. Mcdaniel, and I. J. Goodfellow, “Transferability in machine learning: from phenomena to black-box attacks using adversarial samples,” ArXiv, vol. abs/1605.07277, 2016. [Online]. Available: https://api.semanticscholar.org/CorpusID:17362994

    2016 arXiv

  142. [150]

    Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,

    P.-Y . Chen, H. Zhang, Y . Sharma, J. Yi, and C.-J. Hsieh, “Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,” in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security , ser. CCS ’17. ACM, ...

    2017

  143. [151]

    Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,

    W. Brendel, J. Rauber, and M. Bethge, “Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,” 2018

    2018

  144. [152]

    Black-box adversarial attacks with limited queries and information,

    A. Ilyas, L. Engstrom, A. Athalye, and J. Lin, “Black-box adversarial attacks with limited queries and information,” in International Conference on Machine Learning , 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:5046541

    2018

  145. [153]

    Assessment of cnn-based methods for individual tree detection on images captured by rgb cameras attached to uavs,

    A. A. dos Santos, J. M. Junior, M. S. Ara ´ujo, D. R. D. Martini, E. C. Tetila, H. L. Siqueira, C. Aoki, A. Eltner, E. T. Matsubara, H. Pistori, R. Q. Feitosa, V . Liesenberg, and W. N. Gonc ¸alves, “Assessment of cnn-based methods for individual tree detection on images captu...

    2019

  146. [154]

    An end-to-end deep learning network for 3d object detection from rgb-d data based on hough voting,

    M. Yan, Z. Li, X. Yu, and C. Jin, “An end-to-end deep learning network for 3d object detection from rgb-d data based on hough voting,” IEEE Access, vol. 8, pp. 138 810–138 822, 2020

    2020

  147. [155]

    Training with noise adversarial network: A generalization method for object detection on sonar image,

    Q. Ma, L. Jiang, W. Yu, R. Jin, Z. Wu, and F. Xu, “Training with noise adversarial network: A generalization method for object detection on sonar image,” in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) . Los Alamitos, CA, USA: IEEE Computer Society, ma...

    2020

  148. [157]

    Adversarial attacks in underwater acoustic target recognition with deep learning models,

    S. Feng, X. Zhu, S. Ma, and Q. Lan, “Adversarial attacks in underwater acoustic target recognition with deep learning models,” Remote Sensing , vol. 15, no. 22, 2023. [Online]. Available: https://www.mdpi.com/2072-4292/15/22/5386

    2023

  149. [158]

    Real-time out-of-distribution detection in learning-enabled cyber-physical systems,

    F. Cai and X. D. Koutsoukos, “Real-time out-of-distribution detection in learning-enabled cyber-physical systems,” arXiv:2001.10494, 2020, https://arxiv.org/abs/2001.10494

    2001 arXiv

  150. [159]

    A baseline for detecting misclassified and out-of-distribution examples in neural networks,

    D. Hendrycks and K. Gimpel, “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” in International Conference on Learning Representations , 2017. [Online]. Available: https://openreview.net/forum?id=Hkg4TI9xl

    2017

  151. [160]

    Enhancing the reliability of out- of-distribution image detection in neural networks,

    S. Liang, Y . Li, and R. Srikant, “Enhancing the reliability of out- of-distribution image detection in neural networks,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=H1VGkIxRZ

    2018

  152. [161]

    A simple unified framework for detecting out-of-distribution samples and adversarial attacks,

    K. Lee, K. Lee, H. Lee, and J. Shin, “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” ArXiv, vol. abs/1807.03888, 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:49667948

    2018 arXiv

  153. [162]

    Auto-encoding variational bayes,

    D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” CoRR, vol. abs/1312.6114, 2013. [Online]. Available: https://api. semanticscholar.org/CorpusID:216078090

    2013 arXiv

  154. [163]

    beta-V AE: Learning basic visual concepts with a constrained variational framework,

    I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “beta-V AE: Learning basic visual concepts with a constrained variational framework,” in International Conference on Learning Representations , 2017. [Online]. Available: https://...

    2017

  155. [164]

    A perceptual metric prior on deep latent space improves out-of-distribution synthetic aperture sonar image classification,

    I. D. Gerg and C. F. Cotner, “A perceptual metric prior on deep latent space improves out-of-distribution synthetic aperture sonar image classification,” in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium , 2023, pp. 6576–6579

    2023

  156. [165]

    Sonar images classification while facing long- tail and few-shot,

    W. Jiao and J. Zhang, “Sonar images classification while facing long- tail and few-shot,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–20, 2022

    2022

  157. [166]

    Outlier detection through null space analysis of neural networks,

    M. Cook, A. Zare, and P. D. Gader, “Outlier detection through null space analysis of neural networks,” ArXiv, vol. abs/2007.01263,

    2007 arXiv

  158. [167]

    Null space analysis for detecting unknown objects during automatic target recognition tasks in sonar data,

    M. Cook, A. Khoury, A. Zare, and P. Gader, “Null space analysis for detecting unknown objects during automatic target recognition tasks in sonar data,” 09 2023, pp. 1–5

    2023

  159. [168]

    Ssd: A unified framework for self-supervised outlier detection,

    V . Sehwag, M. Chiang, and P. Mittal, “Ssd: A unified framework for self-supervised outlier detection,” 2021

    2021

  160. [169]

    Available: https://api.semanticscholar.org/CorpusID: 220301711

    [Online]. Available: https://api.semanticscholar.org/CorpusID: 220301711

  161. [170]

    D. J. C. MacKay, Information Theory, Inference, and Learning Algo- rithms. Copyright Cambridge University Press, 2003

    2003

  162. [171]

    C. E. Rasmussen, Gaussian Processes in Machine Learning . Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, pp. 63–71. [Online]. Available: https://doi.org/10.1007/978-3-540-28650-9 4

    2004 doi

  163. [172]

    React: Out-of-distribution detection with rectified activations,

    Y . Sun, C. Guo, and Y . Li, “React: Out-of-distribution detection with rectified activations,” in Neural Information Processing Systems ,

  164. [173]

    Available: https://api.semanticscholar.org/CorpusID: 244709089

    [Online]. Available: https://api.semanticscholar.org/CorpusID: 244709089

  165. [174]

    What uncertainties do we need in bayesian deep learning for computer vision?

    A. Kendall and Y . Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” in Proceedings of the 31st Inter- national Conference on Neural Information Processing Systems , ser. NIPS’17. Red Hook, NY , USA: Curran Associates Inc., 2017, p. 5580–5590

    2017

  166. [175]

    Dropout as a bayesian approximation: representing model uncertainty in deep learning,

    Y . Gal and Z. Ghahramani, “Dropout as a bayesian approximation: representing model uncertainty in deep learning,” in Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48, ser. ICML’16. JMLR.org, 2016, p. 1050–1059

    2016

  167. [176]

    A review of uncertainty quan- tification in deep learning: Techniques, applications and challenges,

    M. Abdar, F. Pourpanah, S. Hussain, D. Rezazadegan, L. Liu, M. Ghavamzadeh, P. Fieguth, X. Cao, A. Khosravi, U. R. Acharya, V . Makarenkov, and S. Nahavandi, “A review of uncertainty quan- tification in deep learning: Techniques, applications and challenges,” Information Fusio...

    2021

  168. [177]

    Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods,

    E. H ¨ullermeier and W. Waegeman, “Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods,” Machine Learning, vol. 110, no. 3, p. 457–506, Mar. 2021. [Online]. Available: http://dx.doi.org/10.1007/s10994-021-05946-3

    2021 doi

  169. [178]

    Deep learning with self-supervision and uncertainty regularization to count fish in underwater images,

    P. Tarling, M. Cantor, A. Clap ´es, and S. Escalera, “Deep learning with self-supervision and uncertainty regularization to count fish in underwater images,” PLoS ONE , vol. 17, no. 5 May, 2022, cited by: 16; All Open Access, Gold Open Access, Green Open Access. [Online]. Avai...

    2022

  170. [180]

    Simple and scalable predictive uncertainty estimation using deep ensembles,

    B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” in Proceed- ings of the 31st International Conference on Neural Information Processing Systems , ser. NIPS’17. Red Hook, NY , USA: Curran Associates ...

    2017

  171. [181]

    Gan-enhanced simulated sonar images for deep learning based detection and classification,

    L. R. Fuchs, A. Nor ´en, and P. Johansson, “Gan-enhanced simulated sonar images for deep learning based detection and classification,” in OCEANS 2022 - Chennai , 2022, pp. 1–5

    2022

  172. [2020]

    Available: https://doi.org/10.1177/1729881420976307

    [Online]. Available: https://doi.org/10.1177/1729881420976307

    doi

  173. [2021]

    Available: https://doi.org/10.5281/zenodo.4608083

    [Online]. Available: https://doi.org/10.5281/zenodo.4608083

    doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.