Authors - Roshani Tawale, Jayshri Todase, Manisha Bharati Abstract - Enforcement of helmet regulations and accurate vehicle identification remain essential components of intelligent traffic management systems. Conventional supervision approaches depend heavily on manual inspection, which is labor-intensive and unsuitable for continuous large-scale monitoring. This study presents an automated framework for helmet violation detection and number plate lo-calization using the YOLOv8 deep learning architecture [3]. The proposed system supports static image analysis, recorded video processing, and live-stream detection within a unified pipeline. Performance is assessed using precision, re-call, and mean Average Precision (mAP@50). Experimental findings demonstrate consistent detection reliability and validate the framework’s applicability for real-time traffic surveillance systems.
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