Abstract Accurate detection and localization of steel corrosion in tunnel infrastructure remains a major challenge, particularly under conditions of variable lighting, limited accessibility, and visual domain shifts common in real‐world inspection scenarios. This study presents a novel integrated framework that automates tunnel inspection by combining self‐supervised deep learning, image‐based three‐dimensional reconstruction, and building information modeling (BIM)‐based spatial damage localization. At the core of our approach is a Segformer‐based, two‐stage domain adaptation model, which leverages pseudo‐labeling and confidence masking to improve generalization across visually diverse environments without requiring extensive labeled data. Unlike traditional supervised methods, our model achieves a mean intersection over union (mIoU) of 0.81 and an F1 score of 0.77, demonstrating superior robustness and generalization. Images captured via unmanned aerial vehicles and iPhones were processed to generate a dense point cloud, which was used to construct a three‐dimensional (3D) BIM model of the tunnel structure. Corrosion regions were detected and precisely localized within the BIM coordinate system using a custom coordinate estimation method. The final outputs were compiled into a structured database for seamless digital asset management. Overall, the proposed framework offers a scalable, cost‐effective, and highly adaptable solution that significantly reduces manual labor and inspection time, with strong potential for broader deployment in infrastructure condition monitoring and digital asset management.
Maharjan et al. (2025) studied this question.