Abstract
All bike riders must now wear helmets due to the high accident rate and hazardous road conditions. Nowadays, wearing a helmet is required. Bike riders occasionally disobey safety regulations, such as not donning a helmet while riding, necessitating human intervention, which has not been very successful. Increasing the use of helmets when riding is the goal of our project. This classification- based model has undergone thorough training. Prior to identifying the number plate, the helmet is first recognized. In order to identify whether the rider is wearing a helmet, CNN recognizes the motorcycle. If the rider is not wearing a helmet, Tesseract OCR will find the motorcycle license plate. As a result, the obtained characters will be kept in the Database. Currently, classified automobiles are retrieved from the database and verified against their original identities, which are saved as details about specific vehicles. Now that the information has been matched, it is further contacted via APIs and messages are sent to the appropriate email or phone number. KEYWORDS: Machine Learning, Object detection, Neural Networks, Database.
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