Traffic Accident Severity Detection Using Deep Learning Approach

Abstract

Road Accidents Are A Major Cause Of Fatalities Worldwide, With Delayed Emergency Response Being A Critical Factor In The Loss Of Lives. Traditional Accident Detection Methods Rely On Manual Reporting, Witness Intervention, Or Emergency Calls, Often Leading To Significant Delays In Providing Medical Assistance And Managing Traffic Congestion. Additionally, Existing Traffic Management Systems Lack The Capability To Automatically Detect Accidents In Real Time And Optimize Emergency Response Routes, Further Exacerbating The Issue. As A Result, There Is An Urgent Need For An Intelligent System That Can Detect Accidents Instantly, Assess Their Severity, And Ensure A Swift Emergency Response. This Project Proposes A Smart Traffic Accident Detection And Automated Emergency Response System Using Deep Learning-based Object Detection Techniques. The System Employs The YOLO (You Only Look Once) Algorithm, A State-of-the-art Object Detection Model, To Analyse Real-time Traffic Camera Footage And Identify Accidents With High Accuracy. Once An Accident Is Detected, The System Evaluates Vehicle Damage To Determine Crash Severity And Automatically Sends Alerts, Including Accident Location And Vehicle Information, To Emergency Responders, Hospitals, And Traffic Control Centers. By Automating Accident Detection And Response, This System Significantly Reduces The Delay In Medical Assistance, Improving Survival Rates And Enhancing Traffic Management Efficiency. By Integrating Artificial Intelligence, Computer Vision, And Real-time Monitoring, The Proposed System Contributes To Road Safety And Supports Smart City Initiatives. The Automated Nature Of The System Ensures Faster Decision-making, Minimizes Human Intervention, And Enhances Overall Emergency Response Effectiveness. Future Enhancements Could Include GPS-based Route Optimization, Vehicle-to-infrastructure (V2I) Communication, And AI-driven Predictive Analytics To Further Improve Accident Prevention And Road Safety.

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