Traffic monitoring systems are a crucial aspect of Intelligent Transportation Systems (ITS). This study utilizes a state-of-the-art deep learning algorithm, YOLOv8, with OpenCV, to address the challenges of vehicle counting and classification. The specific research area chosen was a highway in Malaysia, which had not been previously investigated in algorithm-based vehicle detection systems. The implementation of this model on the highway is essential for enhancing road safety and preventing accidents. Our findings reveal that the model achieved a remarkable 96% accuracy in detecting vehicles and 94.08% accuracy in classifying them. As a result, it is recommended to implement this model on the highway to improve traffic management and increase overall safety.


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    Titel :

    Vehicle Class Detection and Counting on a Malaysian Road Using YOLOv8 and OpenCV


    Beteiligte:
    Hassan, Mahbub (Autor:in) / Mahin, Hridoy Deb (Autor:in) / Jusoh, Muzammil (Autor:in) / Nafees, Abdullah Al (Autor:in) / Paul, Arpita (Autor:in) / Islam, Md Ashequl (Autor:in)


    Erscheinungsdatum :

    26.08.2024


    Format / Umfang :

    618326 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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