Abstract
Traffic violations are increasing due to rapid urbanization and an increase in the number of vehicles per capita creating accidents and congestion on the road. Most of the traditional methods for traffic violation detection are error-prone and manual involvement doesn’t achieve effective results. The proposed system uses YOLOv8 for the real-time detection of riders, helmets and motorcycles and also OCR for automatic license plate recognition. Spatial correlation theory has been applied to detect most common issues like helmet and triple riding violations. The proposed system achieved a precision of 89.91% for helmet detection and 76.63% for triple riding detection demonstrates effectiveness in real-world traffic situations.
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