One of the primary criteria for autonomous cars and most advanced driving assistance systems is the capacity to perceive and interpret all static and dynamic objects around the vehicle under diverse driving and climatic circumstances (ADAS). Convolutional neural networks have the potential to give safe ADAS in current vehicles (CNN). In this research, we describe a YOLO based traffic sign identification system that has been enhanced using a CNN. As real-time detection is necessary for safe driving, the YOLO network utilized in this study was pre-trained to identify and classify just five items, which are divided into categories such as automobiles, trucks, people, traffic signs, and traffic lights.


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    Titel :

    Traffic Sign Recognition System Using YOLO: Societal System for Safe Driving


    Beteiligte:
    Pawar, Prashant M. (Herausgeber:in) / Ronge, Babruvahan P. (Herausgeber:in) / Gidde, Ranjitsinha R. (Herausgeber:in) / Pawar, Meenakshi M. (Herausgeber:in) / Misal, Nitin D. (Herausgeber:in) / Budhewar, Anupama S. (Herausgeber:in) / More, Vrunal V. (Herausgeber:in) / Reddy, P. Venkata (Herausgeber:in) / Vayadande, Kuldeep (Autor:in) / Patil, Rohit (Autor:in)

    Kongress:

    Techno-Societal 2016, International Conference on Advanced Technologies for Societal Applications ; 2022 ; Mumbai, India December 09, 2022 - December 10, 2022


    Erschienen in:

    Techno-Societal 2022 ; Kapitel : 16 ; 157-166


    Erscheinungsdatum :

    19.11.2023


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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