A visual inspection and diagnosis system for bridge rivets based on a convolutional neural network
Tengjiao Jiang, Gunnstein T. Frøseth, Anders Rønnquist, Xuan Kong, Lu Deng
Abstract
Rivets are critical mechanical fasteners in steel bridges, and rivet defects may cause catastrophic failure. This study proposes a convolutional neural network (CNN)-based inspection system for fast rivet identification and diagnosis. Rivet states are classified as normal, rusted, loose, and missing. A CNN-based training workflow was introduced to develop a reliable rivet diagnosis system. A multiscale moving window searching technique was proposed to solve the challenge of small rivet identification. A continuous dataset enrichment strategy was applied, which improves training efficiency and minimizes training time. The model performance was assessed based on a historical bridge in Gjerstad. The proposed multiscale moving window searching technique significantly enhances the rivet identification rate to 96.3%. The classification accuracy and model robustness were evaluated, and conditions leading to unidentified rivets were discussed and summarized.