Collaborative Real-Time Single-Object Anomaly Detection Framework of Roadside Facilities for Traffic Safety and Management Using Efficient YOLO

This paper proposes an Edge AI-based collaborative framework for real-time anomaly detection of roadside facilities to enhance traffic safety and management. Traditional detection methods rely on fixed cameras or manual inspections, which are time-consuming and inefficient. Our approach embeds lightweight YOLO models in vehicle dashboard cameras to collect and analyze diverse video data across multiple vehicles in real time. This distributed system overcomes the limitations of individual vehicles through collaborative data aggregation and enables robust anomaly detection in various types of roadside facilities. We evaluate several YOLO variants to identify the optimal balance between detection accuracy and computational efficiency. Experimental results demonstrate improved anomaly detection precision and faster response times, validating the feasibility of our system for practical deployment. The proposed method offers a scalable and efficient solution for proactive traffic management and accident prevention by leveraging distributed edge intelligence.