Real Time Vehicle Collision Detection with Alert System

“Accident detection is an essential application in intelligent transportation systems for the safety of drivers and passengers. In recent years, deep learning-based object detection models have been leveraged to achieve significant improvements in real-time object detection. YOLO (You Only Look Once) stands out as one of these models due to its real-time performance and high accuracy. In this paper, we propose an accident detection system using YOLOv5, a state-of-the-art version of YOLO. The system is designed to detect three types of accidents, namely vehicle rollover, rear-end collision, and head-on collision. We have employed a pre-trained YOLOv5 model, initially trained on the COCO dataset, and fine-tuned it using a custom dataset of accident images. The proposed system achieves an average precision of 0.94 for vehicle rollover detection, 0.93 for rear-end collision detection, and 0.92 for head-on collision detection. It also demonstrates promising real-time performance, with an average processing time of 0.03 s per frame on an NVIDIA GeForce GTX 1080 Ti GPU. This system can be seamlessly integrated into intelligent transportation systems to provide real-time accident detection and alerting, significantly enhancing the safety of drivers and passengers on the road.”