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UAV Aerial Photography Traffic Object Detection Based on Lightweight Design and Feature Fusion

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Biometric Recognition (CCBR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13628))

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Abstract

Real-time and accurate traffic object detection is one of the important technologies to support intelligent traffic management. Focusing on the problem of poor detection accuracy in aerial photography scenes of drones, the algorithm of lightweight design and multi-scale feature fusion for traffic object detection based on YOLOv5s (LMF-YOLOv5s) is proposed. First, a lightweight network is designed. Then a fast-spatial pyramid convolution module based on dilated convolution is constructed. Finally, an improved spatial pyramid pooling layer module is introduced before the detection layers of different scales, which can enhance the multi-scale feature fusion ability of the network. The experimental results in the public dataset VisDrone show that the detection accuracy of the proposed method is improved by 7.4% compared with YOLOv5s. The model's parameters are reduced by 67.3%, and the model's size is reduced by 63.2% compared with YOLOv5s.

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References

  1. Zhao, P., Hu, H.: Geographical patterns of traffic congestion in growing megacities: big data analytics from Beijing. Cities 92, 164–174 (2019)

    Article  Google Scholar 

  2. Bhaskaranand, M., Gibson, J.D.: Low-complexity video encoding for UAV reconnaissance and surveillance. In: 2011-MILCOM 2011 Military Communications Conference, pp. 1633–1638. IEEE Press, New York (2011)

    Google Scholar 

  3. Madawalagama, S., Munasinghe, N., Dampegama, S.D.P.J., Samarakoon, L.: Low cost aerial mapping with consumer-grade drones. In: 37th Asian Conference on Remote Sensing pp. 1–8 (2016)

    Google Scholar 

  4. Sa, I., Hrabar, S., Corke, P.: Outdoor flight testing of a pole inspection UAV incorporating high-speed vision. In: Mejias, L., Corke, P., Roberts, J. (eds.) Field and Service Robotics, pp. 107–121. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-07488-7_8

    Chapter  Google Scholar 

  5. El-Sayed, H., Chaqfa, M., Zeadally, S., Puthal, D.: A traffic-aware approach for enabling unmanned aerial vehicles (UAVs) in smart city scenarios. IEEE Access 7, 86297–86305 (2019)

    Article  Google Scholar 

  6. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Press, New York (2014)

    Google Scholar 

  7. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448. IEEE Press, New York (2015)

    Google Scholar 

  8. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, 28. (2015)

    Google Scholar 

  9. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125. IEEE Press, New York (2017)

    Google Scholar 

  10. Liu, W., et al.: SSD: single shot Multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  11. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788. IEEE Press, New York (2016)

    Google Scholar 

  12. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988. IEEE Press, New York (2017)

    Google Scholar 

  13. Zhang, P., Zhong, Y., Li, X.: SlimYOLOv3: narrower, faster and better for real-time UAV applications. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 37–45. IEEE Press, New York (2019)

    Google Scholar 

  14. Wang, X., Li, W., Guo, W., Cao, K.: SPB-YOLO: an efficient real-time detector for unmanned aerial vehicle images. In: 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 099–104. IEEE Press, New York (2021)

    Google Scholar 

  15. Sun, W., Dai, L., Zhang, X., Chang, P., He, X.: RSOD: real-time small object detection algorithm in UAV-based traffic monitoring. Appl. Intell. 52(8), 8448–8463 (2021)

    Article  Google Scholar 

  16. Redmon, J., & Farhadi, A.: YOLOv3: An Incremental Improvement. arXiv preprint arXiv:18 04.02767 (2018)

    Google Scholar 

  17. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  18. Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: PANet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9197–9206. IEEE Press, New York (2019)

    Google Scholar 

  19. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

    Article  Google Scholar 

  20. Benjumea, A., Teeti, I., Cuzzolin, F., & Bradley, A.: YOLO-Z: improving small object detection in YOLOv5 for autonomous vehicles. arXiv preprint arXiv:2112.11798 (2021)

  21. Zhu P, Du D, Wen L, et al.: VisDrone-VID2019: the vision meets drone object detection in video challenge results. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 227–235. IEEE Press, New York (2019)

    Google Scholar 

  22. Ghiasi, G., Lin, T.Y., Le, Q.V.: NAS-FPN: learning scalable feature pyramid architecture for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7029–7038. IEEE Press, New York (2019)

    Google Scholar 

  23. Li, C., Yang, T., Zhu, S., Chen, C., Guan, S.: Density map guided object detection in aerial images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 737–746. IEEE Press, New York (2020)

    Google Scholar 

  24. Yang, F., Fan, H., Chu, P., Blasch, E., Ling, H.: Clustered object detection in aerial images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8311–8320. IEEE Press, New York (2019)

    Google Scholar 

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Acknowledgments

This work was supported by the National Key R&D Program Funding Project (No. 2020YFC1512601); Hefei Natural Science Foundation Project (No. 2022015).

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, China

    Xuesen Ma, Tianbao Zhou, Ji Ma, Gonghui Jiang & Xuemei Xu

  2. Engineering Research Center of Safety-Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei, China

    Xuesen Ma, Tianbao Zhou, Ji Ma, Gonghui Jiang & Xuemei Xu

  3. Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology), Hefei, China

    Gonghui Jiang & Xuemei Xu

Authors
  1. Xuesen Ma
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  2. Tianbao Zhou
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  3. Ji Ma
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  4. Gonghui Jiang
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  5. Xuemei Xu
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Corresponding author

Correspondence to Xuesen Ma .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Weihong Deng

  2. Tsinghua University, Beijing, China

    Jianjiang Feng

  3. Beihang University, Beijing, China

    Di Huang

  4. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Meina Kan

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  6. Tsinghua University, Beijing, China

    Fang Zheng

  7. China Electronics Standardization Institute, Beijing, China

    Wenfeng Wang

  8. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaofeng He

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Ma, X., Zhou, T., Ma, J., Jiang, G., Xu, X. (2022). UAV Aerial Photography Traffic Object Detection Based on Lightweight Design and Feature Fusion. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_69

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  • DOI: https://doi.org/10.1007/978-3-031-20233-9_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20232-2

  • Online ISBN: 978-3-031-20233-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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