Smart Traffic Violation Detection and Reporting System

Boggala Prudhvi Teja, Dr.S.Usharani

Traffic Violation Detection; YOLOv8; Computer Vision; ANPR; Deep Learning; Smart Traffic System.

The rapid increase in urban vehicle density has posed significant challenges to traditional traffic management systems, which rely heavily on manual monitoring and enforcement. These conventional approaches are often inefficient, prone to human error, and incapable of providing continuous surveillance across large-scale road networks. To address these limitations, this paper presents a Smart Traffic Violation Detection and Reporting System that leverages advancements in computer vision, deep learning, and web technologies to automate traffic law enforcement processes.The proposed system utilizes the YOLOv8 object detection model for real-time identification of vehicles and detection of traffic violations such as signal jumping, over-speeding, helmet violations, triple riding, and wrong-way driving. Automatic Number Plate Recognition (ANPR) is implemented using EasyOCR to extract vehicle registration numbers from captured frames. The system further integrates an AI-based event summarization module that generates structured and human-readable violation reports to assist enforcement authorities. A Django-based web application serves as the backend framework, enabling efficient data management, secure user authentication, and real-time monitoring through an interactive dashboard. The system also incorporates analytics features to identify traffic violation trends and hotspots, facilitating data-driven decision-making. The entire architecture is built using open-source tools such as Python, OpenCV, PyTorch, and Chart.js, ensuring scalability and cost-effectiveness. The proposed solution enhances traffic monitoring efficiency, reduces dependency on manual intervention, and contributes to improved road safety by enabling consistent and accurate enforcement of traffic regulations.

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