Smart Vision: AI-Powered Traffic Violation Detection Using YOLOv7

Traffic violations are a major contributor to road accidents and fatalities, especially in densely populated urban regions. Common violations such as jumping red lights, triple riding on two-wheelers, reckless driving, and riding without helmets significantly increase the likelihood of accidents. Conventional traffic monitoring systems largely depend on manual supervision by traffic police or basic sensor-based methods, which are often inefficient, time-consuming, and susceptible to human error. To overcome these limitations, intelligent traffic monitoring systems based on computer vision and deep learning have gained increasing importance. This paper presents a deep learning-based automated traffic violation detection system using the YOLOv7 object detection model. The proposed system processes video streams captured from roadside surveillance cameras and analyses them frame by frame to detect various traffic violations. The YOLOv7 model is used to identify vehicles and generate bounding boxes around detected objects. A predefined threshold line is applied to determine whether a vehicle crosses the signal during a red light, thereby detecting signal violations. Additionally, the system identifies overloading or triple riding on two-wheelers by analysing the number of riders within a single vehicle bounding box. Helmet violations are also detected by determining whether riders on motorcycles are wearing helmets. If a rider is identified without a helmet, the system classifies it as a violation. The system utilizes publicly available datasets such as the MS COCO dataset for vehicle detection and a custom annotated dataset for detecting overloading and helmet violations. The model is trained and evaluated using performance metrics including precision, recall, F-measure, and mean Average Precision (mAP). Experimental results indicate that the proposed system can accurately detect multiple traffic violations while maintaining efficient real-time performance. The proposed approach offers a cost-effective, automated, and scalable solution for traffic monitoring. It can assist traffic authorities in improving road safety and reducing the burden of manual monitoring. Furthermore, the system can be integrated with existing smart city surveillance infrastructure to support intelligent transportation management and law enforcement.