With the accelerating urbanization process, road intersections, as critical nodes in transportation networks, face increasing traffic pressure due to pedestrian and vehicle violations, making order management particularly crucial. This study proposes a computer vision-based red-light violation detection system for urban intersections. Featuring a modular architecture, the system incorporates the YOLOv5 deep learning algorithm and OpenCV technology to achieve real-time video monitoring. Capable of operating in complex traffic scenarios, it demonstrates rapid and accurate identification of pedestrians and vehicles, enabling timely detection and processing of red-light running violations. Experimental results demonstrate a 95% accuracy rate in recognizing pedestrian and vehicle violations. The implemented system enhances traffic safety at urban intersections while optimizing traffic flow management efficiency.


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    Title :

    Computer Vision-Based Traffic Monitoring: Design of a Red-Light Violation Detection System for Urban Intersections


    Contributors:
    Yang, Lei (author) / Si, Jiangqian (author) / He, Pengju (author) / Zhao, Lang (author)


    Publication date :

    2025-05-16


    Size :

    2217053 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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