AI Powered Road Hazard Detection and Reporting using Machine Learning

With the rise in road traffic accidents, there is a growing need for intelligent systems that can detect incidents promptly and alert the concerned authorities without relying on human intervention. This project presents an AI-powered accident detection and reporting system that utilizes YOLO (You Only Look Once), a real-time object detection algorithm, to automatically identify vehicular accidents from live CCTV camera feeds. The system is designed to continuously monitor intersections or roadways, and when an accident is detected, it immediately triggers an alert by sending an HTTP request to a specified URL endpoint (e.g., local host: 3000). A separate Python-based listener module runs on the backend, continuously listening to this port. Upon receiving the detection alert, it notifies the control room or relevant personnel, allowing them to take immediate action such as dispatching emergency services or informing nearby hospitals. This ensures faster response times and improves the chances of saving lives. The entire workflow is automated from accident detection to alert generation minimizing human delay and enhancing road safety. The system is also lightweight and can be deployed on edge devices connected to surveillance infrastructure. In essence, this project aims to provide a low-latency, high-accuracy, real-time accident monitoring system, thereby offering a practical and scalable solution for modern smart city transportation networks.