Real-Time Accident Detection and Response System Using Resnet Algorithm

As urbanization expands rapidly, traffic accidents have become one of the greatest environmental scourges, leading to the loss of life, economic loss, and public safety. A real-time accident detection and response mechanism is proposed that utilizes the ResNet deep learning algorithm to automatically identify and classify accidents based on the severity based on streaming camera feed. ResNet is so capable of feature extraction that accidents are classified with almostabsolute accuracy: minor or major ones. In the case of a major accident, messages are sent automatically to the police and the nearest hospitals, with the reminder email being sent if action is not taken within seconds, ensuring a relatively swift response. In the case of minor accidents, reports are made to the police immediately. Furthermore for future work our system will have an integrated human detection module based on SSD-MobileNet counts the bystanders at the accident scene and provides valuable information on public response. The proposed approach uses one of the most contemporary deep learning algorithms to improve detection classification and to ensure a completely automated alert system that enables timely and coordinated emergency response, reducing response time and contributing towards safer roads.