Huang, I-FengI-FengHuangShih, Kuan-ChunKuan-ChunShihYING-CHIEH CHANChen, Po-HanPo-HanChenChang, Luh-MaanLuh-MaanChang2026-03-122026-03-12202613467581https://www.scopus.com/record/display.uri?eid=2-s2.0-105028000389&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/736228Steel bridges are highly susceptible to corrosion, which threatens structural safety and requires reliable inspection methods. Traditional manual inspections lack accuracy, consistency, and real-time capability. This study proposes an efficient rust detection framework that integrates HSV color space transformation, a U-Net (ResNet-34) deep learning model, and a webcam. To address the challenge of annotating dispersed rust spots, HSV-based auxiliary labeling was applied, with manual supplementation for cases involving light/dark rust colors. Experimental results demonstrate that the proposed method significantly improves labeling efficiency while maintaining high accuracy. The model achieved IoU of 0.69–0.87, and F-measure of 0.93–0.99 across different scenarios, outperforming approaches such as LS-SVM, ANLDR, and ANNRI. Integrated with a webcam, the system completed rust detection in approximately 7.5 seconds per image, enabling real-time application. The findings confirm that even with a relatively small dataset, U-Net (ResNet-34) provides fast, accurate, and stable rust detection, highlighting its potential for practical steel bridge monitoring and maintenance.trueBridge inspectiondeep learningrust detectionsteel bridge maintenanceU-Net (ResNet-34)journal article10.1080/13467581.2026.26134922-s2.0-105028000389