Traffic sign detection and recognition (TSDR) is a pivotal component of contemporary transportation systems, designed to enhance road safety and operational efficiency. In an era where automation is indispensable, TSDR stands out as a critical tool for optimizing driving experiences. This study presents the development of a TSDR model employing advanced deep learning techniques, with a particular emphasis on convolutional neural networks (CNNs). Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which comprises annotated images of various traffic signs captured under diverse environmental conditions, we achieved remarkable results. Our CNN models demonstrated a 99% detection rate, underscoring their efficacy in traffic sign recognition tasks. Furthermore, transfer learning models yielded impressive accuracies, with VGG19 achieving 99.64%, EfficientNetB7 reaching 99%, and ASNet attaining 99.72%. Despite these promising results, we identified several limitations, including dataset-specific dependencies and the need for further enhancements in model design and training methodologies. Addressing these challenges is necessary for ensuring the robustness and reliability of intelligent transportation systems, thereby significantly improving road traffic management, safety, and efficiency.


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    Titel :

    Traffic sign recognition based on CNN vs different transfer learning techniques


    Beteiligte:
    Pang, Xiaodan (Herausgeber:in) / Ghouse, Mohammed (Autor:in) / Farag, Saber (Autor:in) / Butt, Usman (Autor:in)

    Kongress:

    Fifth Symposium on Pattern Recognition and Applications (SPRA 2024) ; 2024 ; Istanbul, Turkey


    Erschienen in:

    Proc. SPIE ; 13540


    Erscheinungsdatum :

    10.02.2025





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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