Helmet and Number Plate Detection

Abstract

An important use of computer technology in recent years has been the automatic helmet recognition of motorcy clists in real-time surveillance film. Deep learning methods are becoming more and more popular as a result, especially for object detection and classification. Nevertheless, a number ofissues, including limited resolution, inadequate lighting, adverse weather, and occlusion, restrict the accuracy of current models in identifying motorcycle helmets. A unique method that makes use of the Faster R-CNN model has been put out to addressthese issues. Using the input image as the starting point, this method first trains the Region Proposal Network (RPN), and then it uses the RPN weights to train the Faster RCNN model. The goal of this method is to increase helmet detection accuracy in live surveillance footage. This method’s experimental results have demonstrated encouraging results, with a 95% accuracy rate in identifying motorcycle helmets in live surveillance footage.This illustrates the promise of deep learning approaches in the field of automatic helmet detection for motorcyclists in real-time surveillance film, as well as the efficacy of the suggested strategy in overcoming the issues encountered by current models.