AI-Powered Road Accident Detection Using Learning

Abstract: Road accidents are a major cause
of injuries, fatalities, and traffic disruptions
worldwide. Timely detection of vehicle
accidents is critical for providing quick
emergency response and reducing the impact
of such incidents. This project presents an
AI-powered real-time Vehicle Accident
Detection system developed using Python,
OpenCV, and Deep Learning techniques. The
system analyzes live video streams or
recorded footage to automatically detect and
classify vehicle accidents.The proposed
approach uses computer vision methods
through OpenCV to process video frames and
extract meaningful visual information. A
Convolutional Neural Network (CNN) is
trained on image and video datasets
containing accident and non-accident
scenarios. The CNN model learns spatial
features such as vehicle movement, collision
patterns, and sudden changes in motion to
accurately identify accident events. Once an
accident is detected, the system can generate
automatic alerts to notify concerned
authorities or emergency services.This
project demonstrates an end-to-end
implementation, starting from dataset preparation and model training to real-time
deployment. The system improves accident
detection accuracy compared to traditional
methods and reduces dependency on manual
monitoring. Overall, this AI-based accident
detection system highlights the effective use
of deep learning and computer vision
technologies to enhance road safety and
support faster emergency response
mechanisms