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A lightweight small object detection algorithm based on improved YOLOv5 for driving scenarios

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Abstract

Small object detection has been a longstanding challenge in the field of object detection, and achieving high detection accuracy is crucial for autonomous driving, especially for small objects. This article focuses on researching small object detection algorithms in driving scenarios. To address the need for higher accuracy and fewer parameters in object detection for autonomous driving, we propose LSD-YOLO, a small object detection algorithm with higher average precision and fewer parameters. Building upon YOLOv5, we fully leverage small-scale feature maps to enhance the network’s detection ability for small objects. Additionally, we introduce a new structure called FasterC3 to reduce the network’s latency and parameter volume. To locate attention regions in complex driving scenarios, we integrate Coordinate Attention and explore multiple solutions to determine the optimal approach. Furthermore, we use a spatial pyramid pooling method called LeakySPPF (Wen and Zhang, in: Jin Z, Jiang Y, Buchmann RA, Bi Y, Ghiran A-M, Ma W (eds.) Knowledge Science, Engineering and Management, pp. 39-46. Springer, Cham, 2023) to further improve network speed, achieving up to 15% faster computation. Finally, to better match driving scenarios, we propose a medium-sized dataset called Cone4k to supplement insufficient categories in the VisDrone dataset. Extensive experiments show that our proposed LSD-YOLO(s) achieves an mAP and F1 score of 24.9 and 48.6, respectively, on the VisDrone2021 dataset, resulting in a 4.6% and 3.6% improvement over YOLOv5(s) while reducing parameter volume by 7.5%.

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Acknowledgements

We would like to thank the laboratory of Capital Normal University for its equipment, teachers’ guidance, and students’ help.

Funding

In this research, we did not use any additional fund support and completely used the original funds of the laboratory.

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Authors and Affiliations

  1. Information Engineering College, Capital Normal University, Beijing, China

    Zonghui Wen, Jia Su, Yongxiang Zhang, Guoxi Gan & Shenmeng Zhang

  2. Nanyang Technological University, Singapore, Singapore

    Mingyu Li

  3. Qingdao University of Science and Technology, Qingdao, China

    Deyu Fan

Authors
  1. Zonghui Wen
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  2. Jia Su
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  3. Yongxiang Zhang
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  4. Mingyu Li
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  5. Guoxi Gan
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  6. Shenmeng Zhang
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  7. Deyu Fan
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Contributions

In this research, Zonghui Wen was responsible for proposal of innovation points, experimental design, implementation, and paper writing; Jia Su and Yongxiang Zhang are responsible for the paper writing guide; Mingyu Li, Guoxi Gan, Shenmeng Zhang, and Deyu Fan conducted part of the experiment.

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Correspondence to Jia Su.

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Wen, Z., Su, J., Zhang, Y. et al. A lightweight small object detection algorithm based on improved YOLOv5 for driving scenarios. Int J Multimed Info Retr 12, 38 (2023). https://doi.org/10.1007/s13735-023-00305-5

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