Automated Helmet Detection and Fine Issuance System Using YOLOv8 & OpenCV

In densely populated countries like India, traffic violations are a major concern, especially among motorcyclists, leading to accidents and significant loss of life and property. A prevalent issue is the failure to wear helmets. To address this, a system called “Helmet Detection for Motorcyclists using YOLOv8 and OpenCV” is proposed, leveraging deep learning to automatically identify and penalize violators in real-time through traffic cameras. The system employs the You Only Look Once (YOLO) algorithm, a deep neural network-based object detection model, to identify riders without helmets. Subsequently, it detects the number plate of violators, extracting its text using the EasyOCR(Optical Character Recognition) library. This extracted data is cross-referenced with a pre-existing database to retrieve the owner's contact details. For automation, an SMS API(Application Programming Interface) is integrated to send fine notifications to the violator's registered mobile number. By encouraging helmet use, the system aims to minimize head injuries during accidents and improve road safety. This automated approach enhances traffic law enforcement, provides evidence of violations, and ensures efficient penalty issuance. Ultimately, the system supports safer roads by promoting compliance with traffic regulations and protecting motorcyclists' lives.