Accident Detection System

This research paper presents an advanced accident detection system utilizing the cutting-edge YOLOv11 algorithm. The system aims to detect and classify vehicular accidents in real-time through traffic surveillance cameras. YOLOv11, the latest iteration in the YOLO family, offers significant improvements in detection accuracy and processing speed compared to its predecessors and other single-stage detectors like SSD. Our implementation achieved a remarkable 96.7% accuracy in accident detection with a false positive rate of only 1.8% and an average detection time of 0.4 seconds. The system demonstrates robust performance across various environmental conditions and traffic scenarios. This research contributes to intelligent transportation systems by providing a reliable, efficient solution for rapid accident detection, potentially reducing emergency response times by up to 8 minutes and improving overall road safety infrastructure. Key Words: Accident detection, YOLOv11, Traffic surveillance, Real-time detection, Road safety, Detection accuracy.