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REVIEW 3 major objections 6 minor 1 cited by

SL-YOLO: A Stronger and Lighter Drone Target Detection Model

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read SL-YOLO lifts drone small-target mAP from 43.0% to 46.9% on VisDrone2019 while cutting parameters from 11.1M to 9.6M.

desk verdict Incremental YOLOv8 variant whose P2 head explains most of the gain; the lightweight modules slightly hurt accuracy, and single-run results don't support component-level claims. read the letter →

arxiv 2411.11477 v3 pith:FK6V6RE2 submitted 2024-11-18 cs.CV

Defan Chen , Luchan Zhang This is my paper · ORCID
classification cs.CV
keywords dronetargetdetectionsmallobjectYOLOv8HEPANfeaturepyramidnetworklightweightVisDrone2019depthwiseseparableconvolution
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

This paper argues that drone-view small-target detection can be materially improved without needing a larger model. On the VisDrone2019 benchmark, the proposed SL-YOLO model raises mean average precision at 0.5 IoU from 43.0% (YOLOv8s) to 46.9% while cutting parameters from 11.1M to 9.6M. The gains come from an added high-resolution detection head, a modified feature-fusion neck called HEPAN, and two lightweight modules. The paper positions this result as useful for real-time drone monitoring in resource-constrained settings.

What carries the argument

The central machinery is the P2 small-target detection head plus the Hierarchical Extended Path Aggregation Network (HEPAN), a neck that adds extra convolutional layers and residual connections between the top-down and bottom-up fusion paths, generating a 160×160 feature map that preserves small-object information usually lost in downsampling. Alongside it, the C2fDCB module (a C2f block rebuilt with depthwise separable convolutions and RepVGGDW reparameterization) and the SCDown module (1×1 channel compression followed by strided depthwise convolution) reduce parameter count and FLOPs. The argument runs through the ablation table, where each component is added to the YOLOv8s baseline and its contribution to accuracy and cost is read off.

What would settle it

Train the models in Table 3 with at least five random seeds and report mean plus standard deviation for each. If the mAP@0.5 difference between YOLOv8s and SL-YOLO, or between adjacent ablation rows, falls within one standard deviation, the claimed 'stronger and lighter' advantage and the step-by-step ablation do not survive.

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Extended reading notes

Core claim

SL-YOLO is a YOLOv8s variant that adds a P2-level detection head producing a 160×160 feature map, replaces the PAN neck with HEPAN (which inserts extra convolutional layers and residual connections into the fusion path), and swaps the C2f block for C2fDCB (depthwise separable convolution combined with RepVGG-style reparameterization), with SCDown handling downsampling. On VisDrone2019-val the full model reaches mAP@0.5 of 46.9% and mAP@0.5:0.95 of 28.9%, up from 43.0% and 26.0% for baseline YOLOv8s, while parameters drop from 11.1M to 9.6M and inference runs at 132 FPS. The ablation attributes most of the gain to the P2 head (+3.2 mAP), with HEPAN adding another +1.0; the lightweight modules trade about 0.3 mAP for a parameter reduction of roughly 1.7M.

Load-bearing premise

Each configuration in the ablation was trained once, so reported gaps of 0.1 to 0.3 mAP points between similar models may be run-to-run noise rather than real architectural differences.

Editorial extensions

If this is right

  • A P2-style detection head alone accounts for most of the small-target gain, so other detector architectures could adopt it without the rest of the SL-YOLO stack.
  • HEPAN's residual-enhanced fusion adds roughly one mAP point on top of the P2 head, suggesting deeper neck fusion helps small objects at a modest cost (+0.7M parameters relative to P2-only).
  • The C2fDCB and SCDown modules together cut 1.7M parameters and 1.4 GFLOPs while losing only 0.3 mAP, a favorable trade for edge deployment.
  • At 9.6M parameters and 132 FPS, SL-YOLO offers a real-time profile comparable to YOLOv8s while outperforming it by 3.9 mAP@0.5 on VisDrone2019-val.
  • On VisDrone2019-test the model trails larger YOLOv9m and YOLOv11m by about 0.5 mAP, so the accuracy claim is strongest against similar-size YOLOv8s models.

Reading between the lines

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

  • If the P2 head is the dominant contributor, then a multi-seed comparison of SL-YOLO against YOLOv8s-p2 (which scores 46.2 mAP at the same 36.7 GFLOPs) would isolate whether HEPAN, C2fDCB, and SCDown add a real edge beyond the head alone.
  • The paper's own data suggest a cost-accuracy frontier: the HEPAN-only configuration reaches 47.2 mAP at 11.3M parameters, while the full SL-YOLO trades 0.3 mAP for a 1.7M parameter cut, meaning a practitioner could choose either point depending on memory constraints.
  • Because the ablation appears to rely on a single run per configuration, the 0.1-point drops from C2fDCB and SCDown fall in the range where training noise matters; reporting variance over several seeds would clarify whether the lightweight modules are truly lossless.
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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. The paper proposes SL-YOLO, an enhancement of YOLOv8s for small-object detection in drone imagery. The proposed architecture adds a P2 small-target detection head, a Hierarchical Extended Path Aggregation Network (HEPAN), and two lightweight modules (C2fDCB and SCDown). On VisDrone2019-val, the authors report mAP@0.5 improving from 43.0% to 46.9% and mAP@0.5:0.95 from 26.0% to 28.9%, while parameters drop from 11.1M to 9.6M, at 132 FPS. The paper also reports comparisons on VisDrone2019-test and an ablation study.

Significance. If the reported gains were robust, SL-YOLO would offer a modest accuracy/size trade-off for drone detection, which is a practically relevant setting. The paper provides a useful empirical comparison across several YOLO variants on both val and test splits, and it reports parameters, GFLOPs, and FPS. However, the central evidence is a set of single-run point estimates with no error bars, and the ablation table contains an internal inconsistency in parameter counts. In its current form, the manuscript does not establish that the proposed components make a reliable contribution beyond the known P2-head modification.

major comments (3)
  1. [Section 4.2 and Table 3] All reported metrics come from a single training run per configuration, with no seeds, error bars, or statistical tests. The ablation deltas that support the paper's central claims are within typical run-to-run variation on a 548-image validation set: HEPAN gives +1.0 mAP@0.5 (46.2 to 47.2), C2fDCB gives -0.2, and SCDown gives -0.1. Without variance estimates, the statement in the abstract that SL-YOLO achieves a 'significant improvement' is not justified. Please report mean and standard deviation over at least 3-5 seeds, or provide a statistical test, for the main comparisons and the full ablation.
  2. [Table 3, row 2] Adding a P2 small-target head to YOLOv8s is reported to reduce parameters from 11.1M to 10.6M while increasing GFLOPs from 28.5 to 36.7. Since a P2 head adds convolutional layers, this decrease is inconsistent with the stated modification and suggests that the ablation rows differ in more than the variable being tested. Please provide the exact module configuration and parameter accounting for each row, and explain why the parameter count decreases when the P2 head is added.
  3. [Abstract, Introduction, and Tables 3, 5, 6] The paper claims the lightweight modules C2fDCB and SCDown reduce model size 'without sacrificing detection capabilities.' The point estimates in Table 3 contradict this: C2fDCB lowers mAP@0.5 by 0.2 and SCDown by another 0.1, and Tables 5 and 6 state that C2f and Conv are respectively slightly better than C2fDCB and SCDown. The claim should be rephrased as an accuracy loss that may be within uncertainty, or the authors should provide evidence that the differences are not statistically meaningful.
minor comments (6)
  1. [Abstract and Introduction] The abstract and introduction use promotional language such as 'revolutionary,' 'pioneering,' 'unparalleled,' and 'break the bottleneck'; these terms are not supported by the modest measured improvements and should be replaced with precise, quantitative descriptions.
  2. [Section 3.2] The text attributes BiFPN to 'Li [30]', but reference [30] is the EfficientDet paper by Tan et al.; please correct the citation.
  3. [Section 4.2] The FPS measurement is described as using 'a single image' and bs=1, but no details are given about warm-up, number of repetitions, or whether the reported FPS is the mean of multiple runs; please describe the protocol and report variance.
  4. [Section 3.3.1] The term 'RepVGGDW convolution' is used but never defined or referenced; please clarify whether this is a standard RepVGG block or a new module, and show its structure.
  5. [Throughout] There are numerous typos and grammatical issues, for example 'we designs the C2fDCB' in the Introduction, 'UA V aerial images' in Section 3.1, and 'also be introduced' in Section 3.2; a thorough proofread is needed.
  6. [Section 4.3, Table 2] The sentence comparing SL-YOLO and YOLOv8s-p2 says 'higher accuracy (37.4% vs 38.3%)' but the order of the numbers is confusing; it should read '38.3% vs 37.4%'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: SL-YOLO is an empirical architecture comparison on a public benchmark; the claimed gains are measured, not encoded in definitions.

full rationale

This paper contains no derivation chain that reduces to its own inputs. The only equations, (1)-(4), are standard parameter and FLOP formulas for standard versus depthwise separable convolutions; they are definitions of quantities being reported and do not assert any accuracy outcome. The central claims (mAP@0.5 43.0 to 46.9, mAP@0.5:0.95 26.0 to 28.9, parameters 11.1M to 9.6M) are direct experimental measurements on the public VisDrone2019 validation and test sets, not fitted quantities or predictions generated by the proposed modules. Ablation Table 3 isolates the P2 head, HEPAN, C2fDCB, and SCDown by re-running the model, so each row is an empirical result. There are no self-citations and no imported uniqueness theorem or ansatz from the authors' prior work; all references are to third-party published methods. The suspicious parameter decrease when adding a P2 head (11.1M to 10.6M in Table 3) and single-run mAP deltas of 0.1-1.0 points are experimental-rigor or comparability concerns, not circularity. Therefore no circular step is identifiable under the stated criteria.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper's central claim rests on four assumptions: standard properties of depthwise separable convolutions, the effectiveness of a P2 detection head, the representativeness of VisDrone2019, and single-run training reliability. There are no invented theoretical entities. The only hand-chosen free parameter is the unspecified HEPAN architecture configuration.

free parameters (1)
  • HEPAN extra convolutional layer configuration
    The number and channel width of additional convolutional layers in HEPAN (Section 3.2, Figure 3c) are not specified; they are hand-chosen architectural choices required to reproduce the architecture and are not justified by analysis.
assumptions (4)
  • standard math Depthwise separable convolution reduces parameters and FLOPs compared with standard convolution at comparable representation capacity
    Invoked in Equations (1)-(4), Section 3.3.1; this is a well-established property from MobileNet/Xception that the paper uses to justify C2fDCB.
  • domain assumption Adding a P2-scale detection head improves small-object detection accuracy
    Section 3.1 relies on this empirical claim; it is a common practice in YOLO variants but is not derived here, and the paper's own Table 3 shows it is the largest source of improvement.
  • domain assumption The VisDrone2019 dataset annotations and split are accurate and sufficiently representative
    All experimental conclusions are drawn from this dataset (Section 4.1); if the annotations or split are biased, the reported comparisons do not generalize.
  • domain assumption Single-run training results are representative enough to compare models
    Section 4.2 describes one training run per model without seeds or error bars; the paper implicitly assumes that observed differences of 0.1-0.3 mAP are not noise.

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Cite this review

Pith. "Pith review of SL-YOLO: A Stronger and Lighter Drone Target Detection Model." pith.science (2026). https://pith.science/paper/FK6V6RE2

@misc{pith2026241111477,
  author       = {Pith},
  title        = {Pith review of: SL-YOLO: A Stronger and Lighter Drone Target Detection Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FK6V6RE2}},
  note         = {Machine review of arXiv:2411.11477}
}
read the original abstract

Detecting small objects in complex scenes, such as those captured by drones, is a daunting challenge due to the difficulty in capturing the complex features of small targets. While the YOLO family has achieved great success in large target detection, its performance is less than satisfactory when faced with small targets. Because of this, this paper proposes a revolutionary model SL-YOLO (Stronger and Lighter YOLO) that aims to break the bottleneck of small target detection. We propose the Hierarchical Extended Path Aggregation Network (HEPAN), a pioneering cross-scale feature fusion method that can ensure unparalleled detection accuracy even in the most challenging environments. At the same time, without sacrificing detection capabilities, we design the C2fDCB lightweight module and add the SCDown downsampling module to greatly reduce the model's parameters and computational complexity. Our experimental results on the VisDrone2019 dataset reveal a significant improvement in performance, with mAP@0.5 jumping from 43.0% to 46.9% and mAP@0.5:0.95 increasing from 26.0% to 28.9%. At the same time, the model parameters are reduced from 11.1M to 9.6M, and the FPS can reach 132, making it an ideal solution for real-time small object detection in resource-constrained environments.

Figures

Figures reproduced from arXiv: 2411.11477 by the authors.

Figure 1
Figure 1. Comparison of detection results between YOLOv8s and SL-YOLO model. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The overall structure of our SL-YOLO model. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The schematic diagrams of network structures: (a) [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The overall structure of the C2fDCB module. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

41 extracted references · 29 canonical work pages · cited by 1 Pith paper

  1. [1]

    Yolov4: Optimal speed and accuracy of object detection, 2020

    Alexey Bochkovskiy, Chien-Yao Wang, and Hong- Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection, 2020. 1, 2 8

    work page 2020

  2. [2]

    Xception: Deep learning with depthwise separable convolutions

    Franc ¸ois Chollet. Xception: Deep learning with depthwise separable convolutions. In CVPR, pages 1251–1258, 2017. 2

    work page 2017

  3. [3]

    Repvgg: Making vgg-style convnets great again

    Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, and Jian Sun. Repvgg: Making vgg-style convnets great again. In CVPR, pages 13733–13742, 2021. 2

    work page 2021

  4. [4]

    Visdrone-det2019: The vision meets drone ob- ject detection in image challenge results

    Dawei Du, Pengfei Zhu, Longyin Wen, Xiao Bian, Haibin Lin, Qinghua Hu, Tao Peng, Jiayu Zheng, Xinyao Wang, Yue Zhang, et al. Visdrone-det2019: The vision meets drone ob- ject detection in image challenge results. In ICCV, pages 0–0, 2019. 2, 6

    work page 2019

  5. [5]

    The pascal visual object classes challenge: A retrospective.IJCV, 111:98–136, 2015

    Mark Everingham, SM Ali Eslami, Luc Van Gool, Christo- pher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes challenge: A retrospective.IJCV, 111:98–136, 2015. 6

    work page 2015

  6. [6]

    Yolov3: An incre- mental improvement

    Ali Farhadi and Joseph Redmon. Yolov3: An incre- mental improvement. In CVPR, pages 1–6. Springer Berlin/Heidelberg, Germany, 2018. 1, 2

    work page 2018

  7. [7]

    Dropblock: A regularization method for convolutional networks

    Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le. Dropblock: A regularization method for convolutional networks. NeurIPS, 31, 2018. 2

    work page 2018

  8. [8]

    Nas-fpn: Learning scalable feature pyramid architecture for object de- tection

    Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le. Nas-fpn: Learning scalable feature pyramid architecture for object de- tection. In CVPR, pages 7036–7045, 2019. 2

    work page 2019

Show all 41 references
  1. [9]

    Fast r-cnn

    Ross Girshick. Fast r-cnn. In ICCV, 2015. 1, 2

    2015

  2. [10]

    Rich feature hierarchies for accurate object detection and semantic segmentation

    Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, pages 580–587, 2014. 1, 2

    2014

  3. [11]

    Ghostnet: More features from cheap opera- tions

    Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, and Chang Xu. Ghostnet: More features from cheap opera- tions. In CVPR, pages 1580–1589, 2020. 2

    2020

  4. [12]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770–778, 2016. 2

    2016

  5. [13]

    Searching for mo- bilenetv3

    Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al. Searching for mo- bilenetv3. In ICCV, pages 1314–1324, 2019. 1

    2019

  6. [14]

    Mobilenets: Efficient convolutional neu- ral networks for mobile vision applications

    Andrew G Howard. Mobilenets: Efficient convolutional neu- ral networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017. 1, 2

    2017 arXiv

  7. [15]

    Densely connected convolutional net- works

    Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kil- ian Q Weinberger. Densely connected convolutional net- works. In CVPR, pages 4700–4708, 2017. 2

    2017

  8. [16]

    Ultralytics yolo

    Glenn Jocher, Jing Qiu, and Ayush Chaurasia. Ultralytics yolo. https://github.com/ultralytics/ultralytics, 2023. 1, 3

    2023

  9. [17]

    Yolov6: A single-stage object detec- tion framework for industrial applications

    Chuyi Li, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke, Qingyuan Li, Meng Cheng, Weiqiang Nie, et al. Yolov6: A single-stage object detec- tion framework for industrial applications. arXiv preprint arXiv:2209.02976, 2022. 1, 3

    2022 arXiv

  10. [18]

    Microsoft coco: Common objects in context

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll´ar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, pages 740–755. Springer, 2014. 6

    2014

  11. [19]

    Feature pyramid networks for object detection

    Tsung-Yi Lin, Piotr Doll ´ar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In CVPR, pages 2117–2125,

  12. [20]

    Path aggregation network for instance segmentation

    Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia. Path aggregation network for instance segmentation. In CVPR, pages 8759–8768, 2018. 1, 2

    2018

  13. [21]

    Learning spa- tial fusion for single-shot object detection

    Songtao Liu, Di Huang, and Yunhong Wang. Learning spa- tial fusion for single-shot object detection. arXiv preprint arXiv:1911.09516, 2019. 2

    1911 arXiv

  14. [22]

    Fully convolutional networks for semantic segmentation

    Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In CVPR, pages 3431–3440, 2015. 2

    2015

  15. [23]

    Shufflenet v2: Practical guidelines for efficient cnn architec- ture design

    Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun. Shufflenet v2: Practical guidelines for efficient cnn architec- ture design. In ECCV, pages 116–131, 2018. 1, 2

    2018

  16. [24]

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

    J Redmon. You only look once: Unified, real-time object detection. In CVPR, 2016. 1, 2

    2016

  17. [25]

    Yolo9000: better, faster, stronger

    Joseph Redmon and Ali Farhadi. Yolo9000: better, faster, stronger. In CVPR, pages 7263–7271, 2017. 1, 2

    2017

  18. [26]

    Faster R-CNN: Towards real-time object detection with re- gion proposal networks

    Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real-time object detection with re- gion proposal networks. In NeurIPS, 2015. 1, 2

    2015

  19. [27]

    Mobilenetv2: Inverted residuals and linear bottlenecks

    Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zh- moginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. InCVPR, pages 4510–4520,

  20. [28]

    Efficientnet: Rethinking model scaling for convolutional neural networks

    Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR,

  21. [29]

    Efficientnetv2: Smaller models and faster training

    Mingxing Tan and Quoc Le. Efficientnetv2: Smaller models and faster training. In International conference on machine learning, pages 10096–10106. PMLR, 2021. 1

    2021

  22. [30]

    Efficient- det: Scalable and efficient object detection

    Mingxing Tan, Ruoming Pang, and Quoc V Le. Efficient- det: Scalable and efficient object detection. In CVPR, pages 10781–10790, 2020. 1, 2, 3

    2020

  23. [31]

    A survey of object detection for uavs based on deep learning

    Guangyi Tang, Jianjun Ni, Yonghao Zhao, Yang Gu, and Weidong Cao. A survey of object detection for uavs based on deep learning. Remote Sensing, 16(1):149, 2023. 1

    2023

  24. [32]

    Attention is all you need

    A Vaswani. Attention is all you need. NeurIPS, 2017. 1

    2017

  25. [33]

    Yolov10: Real-time end- to-end object detection

    Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jun- gong Han, and Guiguang Ding. Yolov10: Real-time end- to-end object detection. arXiv preprint arXiv:2405.14458 ,

    arXiv

  26. [34]

    Cspnet: A new backbone that can enhance learning capability of cnn

    Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh, and I-Hau Yeh. Cspnet: A new backbone that can enhance learning capability of cnn. In CVPR, pages 390–391, 2020. 2

    2020

  27. [35]

    Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

    Chien-Yao Wang, Alexey Bochkovskiy, and Hong- Yuan Mark Liao. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In CVPR, pages 7464–7475, 2023. 1, 3

    2023

  28. [36]

    Yolov9: Learning what you want to learn using pro- grammable gradient information, 2024

    Chien-Yao Wang, I-Hau Yeh, and Hong-Yuan Mark Liao. Yolov9: Learning what you want to learn using pro- grammable gradient information, 2024. 1, 3 9

    2024

  29. [37]

    Deep learning for unmanned aerial vehicle-based object de- tection and tracking: A survey

    Xin Wu, Wei Li, Danfeng Hong, Ran Tao, and Qian Du. Deep learning for unmanned aerial vehicle-based object de- tection and tracking: A survey. IEEE Geoscience and Re- mote Sensing Magazine, 10(1):91–124, 2021. 1

    2021

  30. [38]

    Aggregated residual transformations for deep neural networks

    Saining Xie, Ross Girshick, Piotr Doll ´ar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In CVPR, pages 1492–1500, 2017. 2

    2017

  31. [39]

    Cutmix: Regu- larization strategy to train strong classifiers with localizable features

    Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix: Regu- larization strategy to train strong classifiers with localizable features. In ICCV, pages 6023–6032, 2019. 2

    2019

  32. [40]

    Shufflenet: An extremely efficient convolutional neural net- work for mobile devices

    Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. Shufflenet: An extremely efficient convolutional neural net- work for mobile devices. In CVPR, pages 6848–6856, 2018. 1

    2018

  33. [41]

    Distance-iou loss: Faster and bet- ter learning for bounding box regression

    Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, and Dongwei Ren. Distance-iou loss: Faster and bet- ter learning for bounding box regression. In AAAI, pages 12993–13000, 2020. 2 10

    2020

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