Deep Learning for Crack Detection in Hydraulic Structures

Taiwan is situated in a subtropical region and is surrounded by the ocean, resulting in abundant rainfall and frequent typhoons. As a result, flood-control infrastructure plays a critical role in disaster mitigation. In addition, Taiwan lies within an active seismic zone, where hydraulic structures such as levees and dams are susceptible to earthquake-induced cracking, potentially impairing flood protection and water-supply functions and increasing overall risk. This study develops a crack-detection system for hydraulic structures using the Mask R-CNN deep learning model. The network was trained with 300 images of hydraulic structures and subsequently evaluated using 50 additional images. The proposed system achieved an accuracy of 80%, precision of 81%, recall of 95%, and an F1-score of 88%. Furthermore, the effects of transfer learning on model performance were investigated. The results indicate that two iterations of transfer learning led to notable improvements across all evaluation metrics, confirming that deep learning approaches can provide accurate and efficient crack detection for hydraulic infrastructure.