ACCIDENT CRASH DETECTION USING DEEP NEURAL NETWORK

This project proposes an Accident Detection and Alerting System that utilizes the YOLO model for real-time object detection. By monitoring live video feeds, the system can identify potential accidents and generate immediate alerts. The system involves training the YOLO model with a custom datasets to optimize accuracy and performance. Evaluation metrics are used to assess system performance, and the system aims to enhance road safety by reducing response times and facilitating efficient resource allocation.The Accident Detection and Alerting System leverages the YOLO model for real-time object detection to monitor video feeds and swiftly detect accidents. It promptly generates alerts that include crucial information such as the accident’s location, timestamp, and a brief description. The YOLO model is trained using a carefully curated datasets to ensure accurate and efficient accident identification. Performance evaluation is conducted using real-world video footage, bench-marking against metrics like precision, recall, and mean average precision. By enabling timely notifications, the system proactively enhances road safety, decreases response times, and facilitates the effective allocation of emergency resources, ultimately contributing to a safer transportation environment.