International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The number of two-wheelers is rapidly increasing. The number of traffic violations and accidents are also becoming substantially higher due to unsafe riding like riding without helmet, triple riding and using mobile phone while riding. The current system for monitoring traffic mainly involves manual monitoring or analog cctvs which affects efficiency and human error, a tedious and time taking the process. To tackle these challenges, the project proposes an AI-Powered Traffic Management System that automatically detects and penalizes traffic violations by two-wheelers, employing the live feed of a webcam or CCTV camera. The YOLOv8 deep learning-based object detection model will be used in the system for quickly detecting helmet absence, triple riding, mobile phone usage while riding, etc. The EasyOCR technique is employed to detect a vehicle’s number plate and then the details of the violation, the details of the rider, date and time, and the fine amount to be charged are all stored in one central location. After the fine is imposed, a system-generated email notification is sent to the vehicle owner containing details of the violation and the amount for which the fine is imposed. All fined records are maintained in the system for monitoring and reporting and for further analysis in the future. The whole system has been developed using Python, OpenCV, Decision Tree and EasyOCR implementing in Visual Studio Code. Moreover, the experimental result shows high accurate, real-time, high accuracy, and reliable detection in practical traffic. The system reduces human interference, automates the traffic rule compliance monitoring, enhances transparency and scalability which boosts road safety and facilitates intelligent and smart traffic management systems (TMS).

Keywords: AI-Powered Traffic Management System, Traffic Violation Detection, YOLOv8, Computer Vision, Helmet and Rider Safety, License Plate Recognition, EasyOCR, Real-Time Video Analysis.

Downloads: | DOI: 10.17148/IJARCCE.2026.15124