These two-wheeler traffic violations in low-light conditions are a major issue in traffic management and road safety. Current methods are mainly focused on one segment of the pipeline (e.g., object detection, low-light enhancement, or reinforcement learning) while failing to incorporate them into a single coherent system. The paper proposes Reinforcement-Aided YOLO-TVT, a novel Adaptive Vision Framework for Low-Light Two-Wheeler Traffic Violation Detection that addresses these limitations. This framework employs sophisticated low-light image enhancement methods along with a personalized YOLO architecture suited for detecting small objects like helmets and license plates, in poor lighting conditions. We incorporate reinforcement learning to allow real-time, adaptive decision-making to improve accuracy and reduce false positives. There are training on data from the perspectives of more than 2 years to Oct 2023. Deployment friendly system, hardware agnostic system which can easily work in low resource stringing environments. The proposed framework serves as a dynamic, privacy-preserving, intelligent solution for automated red-light traffic enforcement, equipped with rigorous privacy protections and capable of interfacing with smart city traffic management systems.


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    Title :

    Adaptive Vision Framework for Low-Light Two-Wheeler Traffic Violation Detection Using Reinforcement-Aided YOLO-TVT


    Additional title:

    Advances in Computer Science res


    Contributors:

    Conference:

    International Conference on Sustainability Innovation in Computing and Engineering ; 2024 ; Chennai, India December 30, 2024 - December 31, 2024



    Publication date :

    2025-05-24


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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