Unsupervised domain adaptation-based crack segmentation using transformer network

Surface cracks are a common structural defect. The intelligent inspection of these defects through computer vision and deep learning is of paramount importance for early maintenance and operation. Despite the remarkable success of supervised learning methods in detecting surface cracks, their performance heavily relies on the availability of extensive labeled datasets. Annotating a single image can be a time-consuming process, prone to human error. Moreover, these methods often struggle to generalize effectively to unseen datasets due to disparities between source and target images. To address this issue, unsupervised domain adaptation comes into play, as it aims to transfer knowledge learned from the labeled source domain to the unlabeled target domain. Consequently, we conducted an evaluation of a recent unsupervised domain adaptation model for semantic segmentation that incorporates masked image consistency into DAFormer, a state-of-the-art model with the ability to adapt to various datasets. To assess the model's performance, we employed three publicly available crack datasets, each containing background and crack classes. Our study has revealed that : (1) SegFormer, a transformer-based model, outperforms ConvNet-based models without utilizing adaptation knowledge, demonstrating superior generalizability to previously unseen data. (2) The unsupervised domain-adaptation model consistently outperforms the source model, resulting in a significant enhancement in the mean intersection over union of SegFormer's source-only approach by a remarkable 10% to 22%. With the exception of a single case, the relative performance of unsupervised domain adaptation compared to supervised training with labeled data exceeds 85%, underscoring its promising performance in crack segmentation. Consequently, our adopted method emerges as a viable alternative, particularly in scenarios where labeled data is scarce or prohibitively expensive.