AI-Based Road Accident Detection Using Deep Learning

Road accidents are one of the major causes of injuries and fatalities worldwide. Early detection of accidents can

significantly reduce response time and emergency services reach the location quickly. This paper proposes an AI-based road

accident detection system using deep learning and computer vision techniques. The system analyzes traffic video footage to

detect vehicles and identify possible accident events. The YOLOv8 object detection model is used to detect vehicles in video frames,

while the Deep SORT tracking algorithm tracks vehicle movements across consecutive frames. By analyzing abnormal motion

patterns such as collisions or sudden stops, the system identifies potential accident events. When an accident is detected, the

system captures the accident frame and generates an alert notification. This approach can help improve road safety by enabling

faster accident detection and response.