Overview
An AI-powered road accident detection and response coordination system that analyzes CCTV surveillance footage using YOLOv11 to identify vehicle collisions across 8+ accident categories. Integrates low-latency video processing, real-time incident alerts via WebSockets, a FastAPI backend, and a Next.js verification dashboard to streamline incident review and emergency response coordination.
About this project
An end-to-end accident monitoring system combining YOLOv11-based computer vision, live CCTV surveillance, real-time event communication, and an incident-verification interface to support faster emergency response.
Contributions
- Developed a real-time vehicle-collision detection system using YOLOv11 and CCTV surveillance footage to identify road accidents across 8+ accident categories.
- Optimized the detection workflow to achieve a reported processing cycle of 8 ms per frame, supporting low-latency video analysis.
- Built a FastAPI backend for accident-detection services and incident event handling.
- Implemented WebSocket-based real-time alerts to communicate detected incidents to the monitoring interface.
- Developed a Next.js and TypeScript frontend with a three-stage incident-verification workflow for reviewing and responding to detected accidents.
- Integrated authentication, PostgreSQL, and Prisma to support application access and persistent data management.
Reported model performance
- mAP@50
99%
Reported object-detection performance across the evaluated accident classes.
- mAP@50–95
89.3%
Reported mean average precision across stricter IoU thresholds.
- Precision
96.8%
Reported proportion of predicted accident detections that were correct.
- Recall
96.4%
Reported proportion of ground-truth accident instances detected.