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.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

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


    Weitere Titelangaben:

    Advances in Computer Science res


    Beteiligte:
    Kannadhasan, S. (Herausgeber:in) / Sivakumar, P. (Herausgeber:in) / Saravanan, T. (Herausgeber:in) / Senthil Kumar, S. (Herausgeber:in) / Vennila, V. (Autor:in) / Savitha, S. (Autor:in) / Kannan, A. Rajiv (Autor:in) / Shanmathi, B. (Autor:in) / Irfan, S. Syed (Autor:in) / Vanmathi, G. (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    24.05.2025


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Traffic Rule Violation Recognition for Two Wheeler using YOLO Algorithm

    R, Baby Chithra / Joy, Salna / Reddy, Ujwal A et al. | IEEE | 2023




    Object Detection Using YOLO Framework for Intelligent Traffic Monitoring

    Amitha, I. C. / Narayanan, N. K. | Springer Verlag | 2021


    Intelligent Traffic Light System Using YOLO

    Sai Venu Prathap, K. / Srinivasulu Reddy, D. / Madhusudhan, S. et al. | Springer Verlag | 2022