As the transportation industry continues to grow and traffic conditions become increasingly complex, ensuring driving safety has become a pressing issue of concern. Traffic sign recognition can offer real-time evaluation of road conditions and driving safety alerts for vehicles and ultimately reduce traffic safety accidents. This study proposes a class-imbalanced traffic sign recognition method based on improved YOLOv7. Firstly, the Coordinate Attention is incorporated into the YOLOv7 backbone to enhance the representation and localization ability of the target to be tested. Then, the larger feature layer is introduced to improve the recognition ability. Finally, the class imbalance problem in the dataset is addressed by using Cost-sensitive softmax cross-entropy loss. The improved YOLOv7 network has been shown through experiments to enhance the detection capability of traffic signs effectively, reaching 90.54% mAP and 86.78% F1-Score.


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    Titel :

    Class-imbalanced traffic sign recognition based on improved YOLOv7


    Beteiligte:
    Batista, Paulo (Herausgeber:in) / Bilas Pachori, Ram (Herausgeber:in) / Wei, Zhuo (Autor:in) / Xia, Jiaoxiong (Autor:in)

    Kongress:

    International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023) ; 2023 ; Changsha, China


    Erschienen in:

    Proc. SPIE ; 12707


    Erscheinungsdatum :

    08.06.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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