Enhanced AI Automated Reporting and Alert System for Road Accidents Using the YOLO Backbone Network

Muhammad Haleem, Ahmad Sohail Raufi

Enhanced AI Automated Reporting and Alert System for Road Accidents Using the YOLO Backbone Network

Muhammad Haleem, Ahmad Sohail Raufi

Volume 1 Issue 1 | Dec 2024

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Abstract

With the increasing number of road accidents globally, as reported by WHO, there are over 50 million casualties annually, including 1.4 million deaths. This study presents an enhanced AI-based automated reporting and alert system for road accident detection using the YOLO (You Only Look Once) backbone network. Our system integrates YOLO's real-time object recognition capabilities with advanced techniques for detecting irregularities, such as sudden changes in speed, abrupt lane changes, erratic driving patterns, or vehicle collisions, to accurately identify and classify road accidents from CCTV footage. The lightweight and efficient YOLOv5 model was chosen to balance computational efficiency and detection accuracy, particularly suitable for resource-constrained environments. This version of YOLO was selected over others due to its faster frame processing rates, reduced computational demands, and robust performance across diverse traffic scenarios, making it ideal for real-time deployment in constrained systems. By combining YOLO's high-speed object detection with event classification algorithms, our solution minimizes false positives by combining spatial-temporal analysis and cross-frame validation. These techniques ensure detected anomalies, such as abrupt movements or collisions, are consistently verified across multiple frames, reducing the likelihood of false triggers and enhancing overall detection precision. Once an accident is detected, the system promptly generates alerts and notifies relevant authorities through automated email notifications, facilitating a timely emergency response. Comprehensive testing on various CCTV setups demonstrates the system's efficiency in diverse traffic scenarios, including varying lighting and weather conditions. Results exhibit significant improvements in accuracy (92.5%), precision (91%), and recall (89%), underlining the system's potential for saving lives by enabling earlier emergency response times.