International Journal of Advanced Innovative Technology in Engineering (IJAITE)

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Accident Detection, YOLOv8, Machine Learning, Road Safety, Object Detection

Prof. Devendra Dandekar, Akanksha Talware, Aadarsh Darokar, Amit Jadhav, Darshana Guntiwar, Swati Dandhare

Abstract :

Road safety has become a top priority in the fast-paced world of today. Route Accidents cause a great deal of sorrow for people by leaving many dead and injured. And monetary losses. Therefore, the creation of sophisticated detecting systems is vital to the outcomes of terrible occurrences. This dissertation investigates the installation and assessment of a YOLOv8-based accident detection system. Neural networks using convolutions (CNN) algorithms. Determining accidents accurately and promptly can greatly lower reaction times for emergencies, perhaps saving lives and lessening the severity of wounds. Conventional accident detection techniques frequently depend on firsthand accounts from people. or manually watching security camera footage, which might be sluggish and inaccurate. In on the other hand, ML approaches offer strong instruments for automating accident detection, providing precise and timely responses.

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