Road safety depends heavily on traffic signs, and as there are more vehicles on the road, the demand for precise traffic sign recognition systems rises. The development of machine-learning algorithms to predict and recognize traffic signs in real-time has been the main focus of research. An overview of the state of advancement in image analysis and classification for recognizing road signs is given in this article. It also goes through their benefits and drawbacks in various traffic situations. Managing various lighting and weather situations, the necessity for real-time processing, and future connection with autonomous driving systems are just a few of the difficulties that are highlighted in the paper. This study seeks to provide guidance to professionals working in the areas of autonomous driving, computer vision, and machine learning who recognize traffic signs.


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    Titel :

    Traffic Sign Detection in the Digital Era: Leveraging Convolutional Neural Networks


    Beteiligte:
    G, Kothai (Autor:in) / Anbumozhi, Anna (Autor:in) / R, Abirami (Autor:in) / R A, Kavin (Autor:in) / M, Karthikeyan (Autor:in) / T, Kalyani (Autor:in) / M, Gokulpandian (Autor:in)


    Erscheinungsdatum :

    12.12.2023


    Format / Umfang :

    1336346 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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