Early Accident Detection Using Deep Learning Models

Abstract

Vehicle accidents rank as the most prominent causes of injury and fatality across the globe. Early detection and response can minimize the losses that may otherwise ensue and enhance safety along roads. The last few years have seen exciting progress in computer vision and machine learning, opening new avenues to attack this major problem. It is in this context that this abstract discusses video-based vehicle accident detection with key techniques, challenges, and future directions. The chief objective of video-based vehicle accidents detection is the automatic identification and classification of accidents from video footage of surveillance cameras, dashcams and other sources. Due to computer vision algorithms and machine learning models, it can conduct real-time and even post-event analysis so that accidents can be detected to allow proper emergency response and help in accident investigation. It explores various methods to identify vehicle accidents, namely Object detection, motion analysis and the deep learning techniques. Object Detection Algorithms, including YOLO V8 (You Only Look Once) and Faster R-CNN (Region-based Convolutional Neural Networks), can enable the detection and localization frames of video, including vehicles and other objects. These algorithms are very much a critical part in accident detection by scrutinizing the dynamics and interactions between Objects; hence, anomalies are identified, which become precursors to an accident.