A COMPREHENSIVE BENCHMARK DATASET FOR TRAFFIC ACCIDENT DETECTION USING YOLOV10

Abstract

The automatic detection of traffic accidents has become an essential focus area in computer vision, propelled by the rapid advancements in autonomous and intelligent transportation systems (ITS). To achieve reliable and real-time detection of accident scenarios, YOLOv10, the latest evolution in the YOLO family, offers a powerful and efficient framework for object detection in complex and dynamic traffic environments. Unlike traditional approaches that struggle with occlusions, varying lighting conditions, and high-speed vehicle motion, YOLOv10 provides superior detection accuracy through its optimized architecture, featuring re-parameterized convolutional layers, decoupled detection heads, and advanced feature fusion mechanisms. These enhancements enable the model to precisely identify accident-related events, such as collisions, overturned vehicles, or lane departures, from surveillance video streams. Index Terms – Traffic Accident Detection, YOLOv10, Computer Vision, Intelligent Transportation Systems, Deep Learning, Real-Time Object Detection, Video Surveillance.