Optimizing AI-Driven Accident Detection System -- 2

AI-Based Road Accident Detection System
I have developed an AI-powered Road Accident Detection System that automatically detects road accidents in real time using CCTV or surveillance camera footage. The system uses a trained YOLOv8 deep learning model to identify accidents from live video streams and immediately generates alerts.
The application includes a modern React frontend, a Node.js/Express backend, and a Flask-based AI service for inference. Accident records, images, and notifications are securely stored using MongoDB and Cloudinary. The system provides a dashboard where users can monitor detected accidents, view incident history, and manage alerts.
Key Features
Real-time accident detection using YOLOv8
Live CCTV/RTSP video stream support
Automatic alert generation
Dashboard for monitoring accidents
Secure user authentication with JWT
Cloud image storage using Cloudinary
MongoDB database integration
Responsive React-based user interface
REST API architecture
Deployable on cloud platforms such as Render and Vercel
Technology Stack
Frontend: React.js, Tailwind CSS
Backend: Node.js, Express.js
AI Service: Python, Flask, YOLOv8, OpenCV
Database: MongoDB Atlas
Storage: Cloudinary
Authentication: JWT
Version Control: Git & GitHub
The project demonstrates practical implementation of Artificial Intelligence, Computer Vision, and Full-Stack Web Development to improve road safety by enabling faster accident detection and emergency response.