Efficient unsupervised domain adaptation for crack segmentation with interpretable Fourier– Morphology blending and Uncertainty-guided self-training

Abstract

Automated crack segmentation models are vital for infrastructure monitoring but fail when deployed in new domains. Overcoming this domain shift without costly re-annotation is vital. This paper presents a novel unsupervised domain adaptation framework that uniquely integrates Fourier-based style transfer with targeted morphological operators and a robust Uncertainty-guided self-training scheme. Specifically, its Fourier–Morphology blending aligns visual styles and crack geometries between domains through controllable image processing operations governed by two intuitive parameters. This is paired with an Uncertainty-guided dual-network training scheme that safely leverages unlabeled target data for robust self-training. Experiments on public and industrial data sets show state-of-the-art performance, improving the $F1$ score by up to 18.5% over competitive baselines in challenging cross-domain scenarios.