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.
Adaptive Vision Framework for Low-Light Two-Wheeler Traffic Violation Detection Using Reinforcement-Aided YOLO-TVT
Advances in Computer Science res
International Conference on Sustainability Innovation in Computing and Engineering ; 2024 ; Chennai, India December 30, 2024 - December 31, 2024
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 24) ; Kapitel : 5 ; 37-51
24.05.2025
15 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Adaptive vision , low-light traffic monitoring , two-wheeler violations , YOLO-TVT , reinforcement learning , real-time detection , small object detection , modular framework , scalable traffic systems , privacy in traffic monitoring , smart city integration , automated traffic enforcement , low-light enhancement , traffic violation detection , real-time adaptation Computer Science , Computer Science, general
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