Accident Detection and Alert System by Using Deep Learning and Edge Computing

The use of artificial intelligence and deep learning models to traffic closed-circuit television (CCTV) systems presents a great deal of promise for the detection and tracking of items on roadways. Nevertheless, it is still difficult to reliably identify traffic accidents and extract useful information from identified items. Early identification of vehicle incidents is essential because it improves incident management systems’ response times and reduces the number of injuries brought on by traffic accidents. In order to enable a service that can aid in preventing accidents involving other cars and traffic congestion, this study provides a deep learning-based algorithm intended to detect anomalous road occurrences. This application is a good fit for deep learning techniques, which have shown remarkable performance in computer vision applications involving intricate feature connections. To identify traffic incidents in video footage, the suggested automated approach makes use of deep learning. The premise behind it is that certain visual characteristics that develop over time might be used to identify traffic accidents. Consequently, the model architecture has a temporal pattern recognition component after a visual feature extraction phase, enabling efficient accident detection in real-time situations.