Abstract: Motorcycles are one of the most popular means for transportation. As the popularity and usage of two-wheelers increase, the number of accidents also inevitably increases. Road accidents are one of the primary causes for non-natural deaths. In order to solve this problem, numerous countries have proposed vehicle laws, making helmets compulsory for both the rider and the passengers. Also, the number of people riding on motorcycles is limited to 2. In India, any person above the age of 4 must compulsorily wear a helmet. Even though wearing a helmet is essential and compulsory, not everybody follows this rule, as there are multiple instances of people not wearing a helmet while driving a two-wheeler. There are also several instances where people do triple riding or riding with more than the allowed number of passengers. To mandate this, we have created a model using OpenCV, TensorFlow and YOLO to identify rule violations. The model takes the front view image and side view image of the vehicle and using object detection techniques, it identifies riders with and without helmet. The model also checks for multiple rider rule violations. If any rule is violated, the licence plates of such riders are automatically extracted and stored

Keywords: Automatic Number Plate Recognition (ANPR), You only look once (YOLO), Helmet Detection, Person detection, Machine Learning (ML), Optical Character Recognition (OCR), Common Objects in Context (COCO)

PDF | DOI: 10.17148/IJARCCE.2021.10806

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