International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Urban traffic management has become one of the most critical challenges facing modern cities as rapid urbanization and exponential growth in vehicle ownership continue to strain existing transportation infrastructure. Traffic rule violations significantly impact road safety and public welfare, and traditional manual monitoring methods are often slow, inconsistent, and dependent on human personnel who suffer from fatigue and limited visibility. To address these challenges, the proposed system leverages deep learning to detect and classify violations from video footage with improved precision and reliability. A comprehensive approach using YOLOv8 for object detection and EasyOCR for license plate recognition was implemented and validated across diverse traffic conditions. The model effectively extracts spatial and temporal features from input video frames and achieves high performance, recording approximately 96.8% vehicle detection accuracy and 95.3% overall violation classification accuracy. The solution is deployed as an interactive web application built with FastAPI, enabling traffic authorities—particularly enforcement officers and urban planners—to upload footage and receive real-time violation alerts. By offering a fast, affordable, and scalable enforcement tool, this work contributes to smarter traffic management practices, timely violation detection, reduced dependency on manual monitoring, and overall enhancement of urban road safety. The study also highlights the potential of YOLOv8-based systems to transform traditional traffic law enforcement through efficient, user-friendly, and technology-driven approaches.

Downloads: | DOI: 10.17148/IJARCCE.2026.15133