AI-Based Road Accident Detection and Alert System

Abstract: Road accidents cause heavy loss of life, and the outcome for victims depends strongly on how quickly emergency services are informed. Manual CCTV monitoring is slow and error-prone. This paper presents the design, architecture and implementation of an AI-based Road Accident Detection and Alert System that analyses CCTV or user- uploaded video frame by frame using Open CV and a YOLO deep-learning detector, and forwards confirmed accidents to emergency services. The system is built with Python, Flask, HTML, CSS and JavaScript and offers a public accident- reporting page with automatic GPS location capture, an automatic video-processing pipeline, an accident review dashboard with statistics, and a separate police/ambulance operations console with live alerts and filters. In the test session the dashboard logged 42 detected events, of which 16 were confirmed on review (38.1 %). The work shows that automated detection with human confirmation and location-tagged alerts can shorten the path from an accident to emergency response, and it identifies precision improvement as the main area for further work.

Keywords: Road Accident Detection, Computer Vision, Deep Learning, YOLO, Open CV, CCTV Surveillance, Emergency Alert System, Django, Intelligent Transportation System.