Intelligent Monitoring System of Two-Wheelers for Enforcing Helmet Regulations on Road

Innovative method to enhance the road safety by detecting of helmet presence and identification of vehicle number plates among scooter and motorcycle riders is presented in this research work. The proposed approach integrates You-only-look-once (YOLO) -V9 for real-time detection of helmets and number plates, with Tesseract Optical-Character-Recognition (OCR) stepping in to extract number plate text if a rider is detected without a helmet. Captured data, including image coordinates and timestamps, are stored for future recognition in a database. Moreover, the proposed model is tested under different harsh weather conditions, such as fog, rain, and dust, establishing vigorous performance even in demanding scenarios and getting a good accuracy of 87%. This system is considered to strengthen compliance with helmet rules and assist enhanced enforcing of traffic laws. Primary trials display encouraging outcomes in accurately discriminating helmet usage and number plates, thus progresses the development of automated traffic surveillance systems.