Abstract Corrosion in infrastructures, such as locks, dams, and facilities, poses significant risks to structural integrity and drives up maintenance costs. Accurate detection and segmentation of corrosion from inspection imagery are critical for proactive structural health monitoring (SHM) and maintenance decision making. Traditional methods for corrosion assessment rely heavily on manual inspection, which are time-consuming, inconsistent, and often ineffective under diverse environmental conditions. Recent advancements in deep learning have improved automated image-based corrosion detection. However, many existing approaches struggle with generalization across domains due to variability in corrosion appearance, surface materials, and imaging conditions (e.g., laboratory versus field). To address this challenge, this paper proposes a deep learning-based corrosion detection framework that integrates domain adaptation to enable robust segmentation performance across heterogeneous datasets. The proposed method consists of four key components: (1) a geometric structure preservation (GSP) module to retain local topological relationships across domains, (2) a singular value decomposition (SVD)-based local discrepancy (SLD) module that leverages SVD to align local feature subspaces at a fine-grained level, (3) a global consistency alignment (GCA) module using maximum mean idscrepancy (MMD) to reconcile distributional shifts between source and target domains, and (4) an auxiliary domain adversarial neural network (DANN) component to further encourage domain-invariant feature learning. These modules are supported by a proposed lightweight segmentation backbone, the efficient segmentation network (EffSegNet), which enables efficient corrosion pattern recognition. The proposed framework was implemented and tested on corrosion imagery from real-world infrastructure assets. The method achieved high segmentation accuracy and demonstrated strong generalization capabilities across domain-shifted datasets, indicating its potential to support scalable, efficient, and automated corrosion assessment for SHM applications.
Wang et al. (Fri,) studied this question.