Beyond the Code

Traffic light violation detection using deep learning

Abstract: Traffic light violation is a major contributor to urban road accidents and traffic inefficiency. Manual monitoring is error-prone and lacks scalability, motivating the need for an automated, intelligent solution. This project presents a deep learning–based traffic light violation detection system that analyzes real-time video feeds from traffic surveillance cameras. The proposed approach uses convolutional neural networks (CNNs) for vehicle detection and tracking, combined with traffic signal state recognition. By synchronizing vehicle position, motion trajectory, and signal phase, the system accurately determines whether a vehicle crosses the stop line during a red signal. Object detection models such as YOLO or Faster R-CNN are employed for high-speed and precise detection, while tracking algorithms ensure continuity across frames. Experimental results demonstrate high detection accuracy under varying lighting and traffic conditions. The system can operate in real time, making it suitable for smart city deployments. This automated framework reduces human intervention, enhances traffic law enforcement, and contributes to safer and more efficient urban transportation systems.