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Neural Network Based on Multi-Scale Saliency Fusion for Traffic Signs Detection

Author

Listed:
  • Haohao Zou

    (School of Electronic and Electrical Engineering, Henan Normal University, Xinxiang 453007, China)

  • Huawei Zhan

    (School of Electronic and Electrical Engineering, Henan Normal University, Xinxiang 453007, China)

  • Linqing Zhang

    (School of Electronic and Electrical Engineering, Henan Normal University, Xinxiang 453007, China)

Abstract

Aiming at recognizing small-scale and complex traffic signs in the driving environment, a traffic sign detection algorithm YOLO-FAM based on YOLOv5 is proposed. Firstly, a new backbone network, ShuffleNet-v2, is used to reduce the algorithm’s parameters, realize lightweight detection, and improve detection speed. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) structure is introduced to capture multi-scale context information, so as to obtain more feature information and improve detection accuracy. Finally, location information is added to the channel attention using the Coordinated Attention (CA) mechanism, thus enhancing the feature expression. The experimental results show that compared with YOLOv5, the mAP value of this method increased by 2.27%. Our approach can be effectively applied to recognizing traffic signs in complex scenes. At road intersections, traffic planners can better plan traffic and avoid traffic jams.

Suggested Citation

  • Haohao Zou & Huawei Zhan & Linqing Zhang, 2022. "Neural Network Based on Multi-Scale Saliency Fusion for Traffic Signs Detection," Sustainability, MDPI, vol. 14(24), pages 1-12, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16491-:d:998482
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    Cited by:

    1. Jiaao Xia & Meijuan Li & Weikang Liu & Xuebo Chen, 2023. "DSRA-DETR: An Improved DETR for Multiscale Traffic Sign Detection," Sustainability, MDPI, vol. 15(14), pages 1-15, July.

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