Abstract: Ensuring road safety through timely detection of traffic accidents is critical for reducing fatalities and improving emergency response times. This project presents a novel system that integrates YOLOv8, a state- of-the-art object detection algorithm, with OpenCV for real-time accident detection on roadways. The system processes live video streams, identifying critical events such as vehicle collisions or abnormal driving behavior. YOLOv8 enables precise and rapid detection of vehicles and pedestrians, while OpenCV enhances image preprocessing and motion analysis. These components are deployed within a Django web framework, providing an interactive interface for monitoring and alerting authorities. By automating the detection process, the solution minimizes human dependency, accelerates response coordination, and contributes to safer traffic environments. This AI-powered approach not only improves detection accuracy but also supports integration into existing traffic management infrastructures, offering a scalable solution for smart city applications.
Keywords: Accident Detection, Road Safety, YOLOv8, OpenCV, Real-Time Object Detection, Deep Learning, Traffic Monitoring, Django Framework, Computer Vision, Emergency Alert System
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DOI:
10.17148/IJARCCE.2025.14556
