Traffic signs are important parts of modern transportation systems because they keep roads safe and help drivers. There are two types of traffic signs: symbol-based and text-based. It is very important for real-world applications like autonomous driving, traffic monitoring, and driver safety to be able to read traffic signs. Therefore, traffic sign identification is a difficult problem since various sizes, illuminations, and sounds impact sign detection and recognition. Traffic sign detection systems based on convolutional neural networks face difficulties in adverse weather conditions, as well as a shortage of training data and difficulty in detecting objects. The training is done using the German Traffic Sign Dataset and resulted in a high rate of traffic sign recognition.


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    Titel :

    An Effective Traffic Sign Image Classification and Detection Using CNN


    Weitere Titelangaben:

    Algorithms for Intelligent Systems


    Beteiligte:
    Asokan, R. (Herausgeber:in) / Ruiz, Diego P. (Herausgeber:in) / Baig, Zubair A. (Herausgeber:in) / Piramuthu, Selwyn (Herausgeber:in) / Mishra, Jayant (Autor:in) / Goyal, Sachin (Autor:in)


    Erscheinungsdatum :

    2022-08-18


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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