Detection and Identification of Non-Helmet Riders and Their License Plate Numbers

Abstract

Abstract: Due to the ever-increasing number of vehicles a variety of traffic regulations are imposed and are addressed using various approaches. Two-wheelers are the most preferred and commonly used vehicles among the youth due to their cost. Bike riders are highly supposed to use helmets, but wearing helmets is often neglected by bike riders leading to accidents and deaths. Riding a bike without a helmet is a traffic offense. While riding a motorcycle/bike, the rider and as well as the pillion rider should wear a helmet according to the traffic rules of road safety. In the current system, traffic offenses are largely monitored by the traffic police by investigating CCTV records. The traffic police zoom into every frame of the CCTV record and tries to identify the license plates of non-helmet riders. This will take a lot of labor and time. The proposed system uses deep learning algorithms, CNN (convolutional neural networks) to analyze real-time video footage from cameras installed in key locations, such as roads and intersections, to identify individuals riding without helmets. The proposed system is expected to significantly reduce the number of non-helmet riding incidents and, therefore, decrease the number of fatalities and serious injuries resulting from motorcycle accidents.