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BDF-YOLOV5: Improved YOLOV5 Based on Bi-directional Fusion Network for Dense Pedestrian Detection

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Communications, Signal Processing, and Systems (CSPS 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1032))

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Abstract

Pedestrian detection is a challenging task in the field of computer vision and plays a crucial role in downstream tasks, such as video surveillance and autonomous driving.Despite significant progress over the past two decades, scale variance and occlusion remain prominent issues. To address these problems, we propose BDF-YOLOv5 in this paper. Based on YOLOv5, we replace the original FPN with the BDF network structure. Furthermore, to further improve our BDF-YOLOv5, we additionally improved the loss function for bounding box regression and proposed weighted-CIOU. Extensive experimental results on the Crowdhuman dataset demonstrate the feasibility of our method. Compared to the baseline model (YOLOv5), BDF-YOLOv5 achieves an improvement of approximately 4.0%.

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References

  1. Girshick RB, Donahue J, Darrell T, Malik J (2013) Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE conference on computer vision and pattern recognition, pp 580–587

    Google Scholar 

  2. Girshick RB (2015) Fast r-cnn. In: 2015 IEEE international conference on computer vision (ICCV), pp 1440-1448

    Google Scholar 

  3. He K, Zhang X, Ren S, Sun J (2014) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37:1904–1916

    Article  Google Scholar 

  4. Liu W, Anguelov D, Erhan D, Szegedy C, Reed SE, Fu C-Y, Berg AC (2015) Ssd: single shot multibox detector. In: European conference on computer vision

    Google Scholar 

  5. Lin T-Y, Goyal P, Girshick RB, He K, Dollár P (2017) Focal loss for dense object detection. In: 2017 IEEE international conference on computer vision (ICCV), pp 2999–3007

    Google Scholar 

  6. Redmon J, Farhadi A (2016) Yolo9000: better, faster, stronger. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 6517–6525

    Google Scholar 

  7. Redmon J, Divvala SK, Girshick RB, Farhadi A (2015) You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 779–788

    Google Scholar 

  8. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. ArXiv, vol. abs/1804.02767

    Google Scholar 

  9. Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: optimal speed and accuracy of object detection. ArXiv, vol. abs/2004.10934

    Google Scholar 

  10. Jocher G, Stoken A, Borovec J, NanoCode012, Chaurasia A, TaoXie, Changyu L, Laughing AV, tkianai, yxNONG, Hogan A, lorenzomammana, AlexWang1900, Hajek J, Diaconu L, Marc, Kwon Y, oleg, wanghaoyang0106, Defretin Y, Lohia A, ml5ah, Milanko B, Fineran B, Khromov D, Yiwei D, Durgesh D, Ingham F (Apr. 2021) Ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations. [Online]. Available: https://doi.org/10.5281/zenodo.4679653

  11. Lin Z, Pei W, Chen F, Zhang D, Lu G (2021) Pedestrian detection by exemplar-guided contrastive learning. IEEE Trans Image Process 32:2003–2016

    Article  Google Scholar 

  12. Hasan I, Liao S, Li J, Akram SU, Shao L (2022) Pedestrian detection: domain generalization, cnns, transformers and beyond. ArXiv, vol abs/2201.03176

    Google Scholar 

  13. Rezatofighi SH, Tsoi N, Gwak J, Sadeghian A, Reid ID, Savarese S (2019) Generalized intersection over union: a metric and a loss for bounding box regression. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 658–666

    Google Scholar 

  14. Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D (2019) Distance-iou loss: faster and better learning for bounding box regression. In: AAAI conference on artificial intelligence

    Google Scholar 

  15. Gevorgyan Z (2022) Siou loss: more powerful learning for bounding box regression. ArXiv, vol abs/2205.12740

    Google Scholar 

  16. Zhou X, Koltun V, Krähenbühl P (2021) Probabilistic two-stage detection. ArXiv, vol abs/2103.07461

    Google Scholar 

  17. Lin T-Y, Dollár P, Girshick RB, He K, Hariharan B, Belongie SJ (2016) Feature pyramid networks for object detection. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 936–944

    Google Scholar 

  18. Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 8759–8768

    Google Scholar 

  19. Tan M, Pang R, Le QV (2019) Efficientdet: scalable and efficient object detection. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 10 778–10 787

    Google Scholar 

  20. Ghiasi G, Lin T-Y, Pang R, Le QV (2019) Nas-fpn: learning scalable feature pyramid architecture for object detection. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 7029–7038

    Google Scholar 

  21. Shao S, Zhao Z, Li B, Xiao T, Yu G, Zhang X, Sun J (2018) Crowdhuman: a benchmark for detecting human in a crowd. ArXiv, vol abs/1805.00123

    Google Scholar 

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Acknowledgements

Fund Project This work was supported by Tianjin Normal University Graduate Research Innovation Project Funding (2023KYCX005Y).

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Authors and Affiliations

  1. College of Electronic Information and Communication Engineering, Tianjin Normal University, Tianjin, 300387, China

    Yuhui Xu & Ruian Liu

Authors
  1. Yuhui Xu
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  2. Ruian Liu
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Corresponding author

Correspondence to Ruian Liu .

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Editors and Affiliations

  1. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  2. Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Xu, Y., Liu, R. (2024). BDF-YOLOV5: Improved YOLOV5 Based on Bi-directional Fusion Network for Dense Pedestrian Detection. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1032. Springer, Singapore. https://doi.org/10.1007/978-981-99-7505-1_52

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  • DOI: https://doi.org/10.1007/978-981-99-7505-1_52

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7539-6

  • Online ISBN: 978-981-99-7505-1

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