Real-Time Helmet and Number Plate Detection and Recognition using YOLOv3

India has a high accident and death rate as a result of widespread non-compliance with helmet legislation, which poses serious difficulties to traffic safety. This research provides a two-step technique for non-helmet rider detection that uses YOLOv3, a potent computer vision technology, to detect license plates and helmets. The technology allows for real-time traffic violation analysis and surveillance, showcasing a high degree of accuracy in detecting safety violations in a variety of settings and topographies. Time and date stamps are combined for effective enforcement and structured data. The system’s dependability in lowering traffic accidents and fatalities has been extensively tested. Furthermore, it has potential uses in urban planning and parking management. This study emphasizes how crucial automated detection systems are to upholding traffic regulations, encouraging adherence, and improving road safety as a result of technical developments.