Deep Learning-Based Intelligent Traffic Violation Detection System Using YOLOv7

Traffic violations have become a major cause of road accidents and fatalities in many countries, particularly in densely populated urban areas. Common violations such as red-light jumping, triple riding on two-wheelers, and reckless driving significantly increase the risk of road accidents. Traditional traffic monitoring systems rely heavily on manual observation by traffic police or limited sensor-based systems, which are inefficient, time-consuming, and prone to human errors. To address these challenges, intelligent traffic monitoring solutions based on computer vision and deep learning have gained significant attention. This paper proposes a deep learning-based automated traffic violation detection system using the YOLOv7 object detection model. The proposed system processes video streams obtained from roadside surveillance cameras and analyses them frame-by-frame to detect different traffic violations. The YOLOv7 model is employed to identify vehicles and generate bounding boxes around detected objects. A predefined threshold line is used to determine whether a vehicle crosses the traffic signal during a red light, thereby identifying signal violations. Additionally, the system detects over boarding or triple riding on two-wheelers by analysing the number of riders detected within a single vehicle bounding box. The system uses publicly available datasets such as the MS COCO dataset for vehicle detection and a custom annotated dataset for over boarding detection. The model is trained and evaluated using performance metrics including precision, recall, F-measure, and mean Average Precision (mAP). Experimental results demonstrate that the proposed model effectively detects multiple traffic violations with high accuracy while maintaining efficient real-time performance. The proposed approach provides a cost-effective, automated, and scalable traffic monitoring solution that can assist traffic authorities in improving road safety and reducing the workload associated with manual monitoring systems. The system can be integrated with existing smart city surveillance infrastructures to enhance intelligent transportation management and law enforcement.