As one of the key technologies of autonomous driving, traffic sign detection and recognition (TSDR) is still a challenging task due to the difficulty and complexity of traffic signs. In addition, the real world often has bad weather conditions, which makes detection more difficult. In this paper, we propose a multi-source traffic sign dataset for foggy scenes, named Foggy-DFG, which contains a large number of street view data for foggy conditions. A YOLOX-CBAM model with attention module is proposed. The attention module improves the performance of YOLOX by fusing and emphasizing the attention of specific locations to emphasize traffic signs under foggy conditions. Experiments on Foggy Driving dataset shows that the proposed method has achieved significant improvement.


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    Titel :

    Traffic Sign Detection and Recognition under Foggy Conditions


    Beteiligte:
    Zhang, Ming (Autor:in) / Li, Cheng (Autor:in) / Chen, Dequ (Autor:in) / Zeng, Hui (Autor:in)


    Erscheinungsdatum :

    11.10.2023


    Format / Umfang :

    2649228 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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