AI Road Accident Detection

This research presents the design and development of a real-time road accident detection system using artificial intelligence, aiming to improve traffic safety and reduce emergency response time. The system leverages advanced computer vision and deep learning techniques to automatically detect road accidents from live video streams captured by traffic surveillance cameras or vehicle-mounted dashcams. The proposed approach integrates a complete processing pipeline that includes video acquisition, frame preprocessing, object detection, and event validation. A deep learning model, based on modern architectures such as Convolutional Neural Networks (CNNs) and YOLO (You Only Look Once), is employed to analyze video frames and identify accident patterns in real time. The system further applies temporal analysis to distinguish actual collision events from normal traffic behavior, reducing false positives. Upon detecting a validated accident, the system triggers an automated alert mechanism that can notify emergency services and store relevant data, including timestamps and video evidence, in a database for further analysis. The architecture is designed to be scalable and adaptable, making it suitable for deployment in smart city environments and intelligent transportation systems. Experimental results, based on comparative analysis with existing studies, demonstrate that the proposed system achieves competitive performance in terms of accuracy and real-time responsiveness, highlighting its potential as a practical solution for modern traffic monitoring and accident prevention. This work contributes to the growing field of AI-driven intelligent transportation systems, providing a framework that combines efficiency, reliability, and real-world applicability. This work was conducted at Arab International University (AIU), Syria. The official website of the university is: https://www.aiu.edu.sy