Abstract

Helmet violation detection is a crucial aspect ofroad safety, as it can significantly reduce the number of fatalities and injuries caused by motorcycle accidents. In recent years, computer vision techniques have been widely used to develop automated systems for helmet violation detection. This project proposes a helmet violation detection system using image processing and machine learning techniques. The proposed system employs computer vision algorithms to detect whether a motorcyclist is wearing a helmet or not. The system is based on a deep learning model, specifically Convolutional Neural Networks (CNN), to classify the input images into two classes, i.e., helmet and nonhelmet. The system is trained on a large dataset of images with differentlighting conditions, backgrounds, and helmet types to enhance its accuracy and generalization ability. The proposed system can be implemented on existing surveillance cameras installed at strategic locations on the road. This system has the potential to increase road safety and reduce the number of motorcycle accidents caused bythe violation of helmet-wearing rules. The system involves person detection, helmet, vs. no-helmet, classification using YOLO algorithm. Convolutional neural network with sequential model is implementing for number plate detection process CNN classification model proposes for classify the number plate in image and extract the user details. Then calculate the fine amount. Finally making SMS services to send alert the users too preventing motorcycle accident.