The use of traffic signs ensures the safety of drivers and pedestrians. A high level of focus is required during driving because of the driver’s need to perform both perceptual and motor functions simultaneously. Inattention or other factors might cause drivers to miss a traffic sign, resulting in an accident. A traffic sign detection and recognition system can assist drivers in locating and following traffic signs. Research on the topic is required since it has become increasingly important to be able to read traffic signs. This is due to the necessity of being able to understand traffic signs. The goal of this research is to find a traffic sign in an image and figure out what kind of sign it is. Traffic sign classification is performed using CNN, InceptionV3, and AlexNet models. We collected 7000 photographs of 70 different types of traffic signs from Bangladesh because there was no comparable data collection from that country’s perspective, resulting in 70 classes in the dataset.


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    Titel :

    Traffic Sign Detection and Recognition Using Deep Learning Approach


    Weitere Titelangaben:

    Lect.Notes Social.Inform.


    Beteiligte:
    Satu, Md. Shahriare (Herausgeber:in) / Moni, Mohammad Ali (Herausgeber:in) / Kaiser, M. Shamim (Herausgeber:in) / Arefin, Mohammad Shamsul (Herausgeber:in) / Rahman, Umma Saima (Autor:in) / Maruf (Autor:in)

    Kongress:

    International Conference on Machine Intelligence and Emerging Technologies ; 2022 ; Noakhali, Bangladesh September 23, 2022 - September 25, 2022



    Erscheinungsdatum :

    11.06.2023


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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