AI-Based Real-Time Road Accident Detection Using Computer Vision and Deep Learning

This research presents the design and development of a real-time road accident detection system using artificial intelligence to enhance traffic safety and reduce emergency response time. The system utilizes computer vision and deep learning techniques to detect accidents from live video streams captured by surveillance cameras or dashcams. The proposed pipeline includes video acquisition, preprocessing, object detection using models such as convolutional neural networks and YOLO (You Only Look Once), and event validation through temporal analysis to reduce false positives. Unlike prior approaches that focus on attention prediction, offline analysis, or computationally intensive spatio-temporal models, this system emphasizes real-time performance and practical deployment. It combines detection accuracy with low-latency processing, making it suitable for intelligent transportation systems and smart city environments. Upon detecting an accident, the system can generate alerts and store relevant data for further analysis. The proposed approach addresses the gap between accuracy and real-time applicability in existing solutions. This work was conducted at Arab International University (AIU), Syria. The official website of the university is: https://www.aiu.edu.sy