Autonomous Traffic Violation Detection Using Machine Learning and Computer Vision

Traffic violations such as red-light jumping, over-speeding, and riding without helmets are among the leading causes of road accidents and fatalities worldwide. With the rapid increase in the number of vehicles in urban areas, traditional traffic monitoring systems based on manual observation and CCTV surveillance have become inefficient, time-consuming, and prone to human error. Existing research has explored various machine learning and deep learning approaches, such as Convolutional Neural Networks (CNN) and YOLO- based object detection models, for detecting specific traffic violations. However, most of these systems are limited to detecting a single type of violation and lack integration for real-time multi- violation detection and data analytics. This paper presents a comprehensive literature review of existing traffic violation detection techniques and identifies key research gaps in scalability, integration, and real-time performance. Based on these gaps, a novel framework is proposed that utilizes deep learning models for detecting multiple traffic violations simultaneously. The system also incorporates a structured database to store violation data and perform analytical reporting. The proposed approach aims to improve accuracy, efficiency, and scalability in intelligent transportation systems and contributes toward the development of smart city traffic management solutions.