VISION-DRIVEN TRAFFIC SAFETY: DETECTING RULE VIOLATIONS WITH DEEP LEARNING MODELS

Abstract

With the rapid growth of urbanization and increasing vehicular density, traffic rule violations have emerged as a major contributor to road accidents, congestion, and public safety risks. Traditional surveillance methods relying on manual monitoring and CCTV review are time-consuming, error-prone, and resource-intensive. To address these challenges, this work proposes a Smart Traffic Enforcement System powered by Deep Learning for the automated detection of traffic violations. The system leverages computer vision and convolutional neural networks (CNNs) to analyze real-time traffic footage and accurately identify violations such as signal jumping, lane discipline breaches, helmet non-compliance, and overspeeding. By integrating advanced image processing with AI-driven classification models, the framework enables high-precision detection and reduces dependency on human intervention. Additionally, the detected violations can be linked to vehicle registration databases for generating automated challans, ensuring transparency and accountability in enforcement. This approach not only strengthens road safety but also supports smart city initiatives by providing scalable, cost-effective, and reliable traffic monitoring. The proposed model demonstrates the potential of deep learning to revolutionize intelligent transportation systems (ITS) and pave the way for data-driven urban traffic governance