With the popularity of vehicles, traffic safety issues are becoming more and more important. In the field of assisted driving and intelligent driving, traffic sign detection is particularly important, which can provide adequate response time for drivers and indirectly protect the safety of people's lives and property. At present, although the development of traffic sign detection algorithms has been relatively mature, there is still some room for improvement. For example, the detection accuracy of traffic signs is not high enough, which is easy to cause false detection and missing detection of signs. Therefore, this paper proposes a traffic sign detection algorithm based on deep learning, which is based on yolov5 model and embedded with SE attention mechanism. transformer module is introduced into the backbone network framework to improve the feature extraction capability of the network. Finally, the weighted BIFPN module is used in the feature fusion network. Experiments show that the improved algorithm proposed in this paper has a good performance in the Chinese traffic sign data set, with the average accuracy of multiple categories increased by 3.6%. The detection accuracy has been greatly improved.


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    Title :

    Traffic sign detection based on deep learning


    Contributors:
    Shen, Linlin (editor) / Zhong, Guoqiang (editor) / Xie, Zhaoyang (author) / Liu, Peilin (author) / Li, Taijun (author)

    Conference:

    Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023) ; 2023 ; Hangzhou, China


    Published in:

    Proc. SPIE ; 12754


    Publication date :

    2023-08-01





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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