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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    Title :

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


    Contributors:


    Publication date :

    2024-08-26


    Size :

    618326 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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