ATSDAS: A Vision-Based System for Automated Traffic Sign Management and Condition Assessment

The maintenance and management of traffic signal assets are crucial for ensuring the safety and smooth operation of road transportation. However, timely repairs and updates of damaged traffic signs remain a significant challenge due to the inefficiency of manual measurements. This study proposes an innovative system that automates traffic sign collection and assessment using deep learning techniques. The system performs three key tasks, including traffic sign detection and tracking, condition assessment, and inventory building, by processing dashboard camera videos with GPS data or open-source street imagery. The system achieves a$\text {mAP}_{50}$of 93.50% for detection and recognition and an accuracy of 93.56% for damage assessment by utilizing a retroreflectivity-related image feature extraction pipeline. The outputs are formalized into a visual inventory, enabling transportation agencies to manage traffic assets more efficiently. Field tests conducted in collaboration with the Washington State Department of Transportation validate the practical effectiveness of the framework. To further address the lack of publicly available condition datasets, this study also introduces the Damaged Traffic Sign Detection and Assessment (DTSDA) dataset, which combines real and synthetic images of damaged signs. Overall, the proposed approach offers a cost-effective and scalable alternative to the traditional labor-intensive method, thereby enhancing traffic asset management efficiency and contributing to safer transportation systems.