A Deep Learning–Based Intelligent Framework for Road Safety Monitoring and Traffic Violation Detection

Abstract

Globally, With the rapid increase in vehicles, traffic rule violations have become a major cause of accidents, congestion, and inefficiency in urban environments. Traditional traffic monitoring systems rely heavily on manual surveillance, which is time-consuming, error-prone, and difficult to scale. Recent advancements in computer vision and deep learning have enabled automated and real-time detection of traffic violations using video surveillance systems. This survey report reviews existing research and technologies related to automated traffic violation detection, with a focus on helmet detection, no- parking detection, lane violation detection, and vehicle tracking systems. The report also presents the current progress of our proposed system, which integrates d–eep learning-based object detection with multi-zone no-parking violation detection and stationary vehicle analysis. The study highlights the strengths, limitations, and research gaps in existing systems and motivates the need for a robust, scalable, and real-time intelligent traffic monitoring framework.