Automated Helmet Detection and E- Challan Generation using YOLOv8

Abstract

One of the primary issues in the contemporary transport system is road safety, especially in those nations where two-wheelers are prevalent. Many accidents are caused by the riders not wearing helmets. The proposed research is an automated helmet detection system and e-challan generation system based on the YOLOv8 deep learning model. The system identifies the violation of helmet through traffic surveillance footage, and automatically identifies vehicle number plates by Optical Character Recognition (OCR). The information of the vehicle extracted is compared with a database to get the owner details and produce an electronic challan. The system suggested can minimize the effort of a manual system and enhance efficiency in the enforcement of traffic laws.
The suggested system will involve the use of YOLOv8 (You Only Look Once version 8) which is the latest and innovative object detection algorithm, to locate the motorcyclists and determine whether they wear helmets or not in real time. The model can identify the riders based on the presence of a helmet on them with a high degree of accuracy and speed by processing live CCTV footage or recorded video streams. The architectural design of YOLOv8 is lightweight and advanced and can be easily used in real-life scenarios, such as dynamic lighting, weather, and traffic conditions.
On detection of a violation, the vehicle number plate is captured by the system and the registration number is extracted with the help of Optical Character Recognition (OCR). This is then cross- tabulated with a vehicle registration database, where the owner will be automatically retrieved. A fine in the form of an e- challan (electronic fine) is then created and delivered to the vehicle owner in the registry via digital means of communication like SMS or email. This is an automated solution that will reduce the human factor in monitoring by traffic officers, minimize human error and ensure that traffic regulations are uniformly enforced. Furthermore, it offers a scalable and effective solution to smart city infrastructure through incorporation of artificial intelligence in traffic management systems.