Accident Detection and Alert System Using Deep Learning

Abstract

Using the YOLO algorithm in the accident detection and reporting system is a creative way to improve road safety. The YOLO (You Only Look Once) approach is an advanced object recognition system that can recognise and locate items in real-time video feeds. The technology uses the YOLO algorithm to identify accidents and alert emergency services, reducing response times and increasing the chance of lifesaving. Object detection and an alarm system are the two key parts of the suggested system. The YOLO method is used by the object detection component to find accidents in live video broadcasts. To correctly recognise accidents, the system is taught using a module of accident photographs. The alert system is turned on when an accident is found. The alert system notifies the emergency services of the accident's location and a brief summary of what happened. A wireless communication network is used to relay this information to the emergency services, speeding up reaction times and raising the possibility of lifesaving. The system has undergone testing on an accident module and photos, and the results are encouraging. The YOLO algorithm was shown to have an accuracy of about 94 in detecting accidents, and the alarm system's response time was under a few seconds. To increase traffic safety and lower the number of accidents, the system can be installed at busy intersections, highways, and other high-risk areas. A possible strategy to increase traffic safety is to implement crash detection and warning systems utilizing deep learning's YOLO technique. In real-time video streams, the system can precisely identify accidents and notify emergency services, speeding up response times and improving the likelihood of lifesaving.