Abstract: Road incidents are among the significant reasons, for the human passing. The majority of the passings in mishaps are because of harm to the top of the bike riders. Among the various sorts of street mishaps, bike mishaps are normal and cause extreme wounds. To reduce the involved risk for the motorcycle riders it is exceptionally fascinating to utilize helmet. The helmet is the motorcyclist's primary security. Many countries require the utilization of caps by motorcyclists, however numerous individuals neglect to comply with the law for different reasons. We present the advancement of a framework utilizing profound convolutional neural networks, (CNNs) for discovering bikers who are disregarding cap rules. The system involves motorcycle, detection, helmet, vs. no-helmet, classification, and method counting. Faster R-CNN with ResNet 50 network, model is implementing for motorcycle detector process. CNN classification model proposes for classify the helmet vs. no-helmet. Finally making alarm sound to alert the officer too preventing motorcycle accident. We assess the framework as far as accuracy and speed.