Self-supervised domain adaptive approach for extrapolated crack segmentation with fine-tuned inpainting generative model

Abstract

The number and proportion of aging infrastructures are increasing, thereby necessitating accurate inspection to ensure safety and structural stability. While computer vision and deep learning have been widely applied to concrete cracks, domain shift issues often result in the poor performance of pretrained models at new sites. To address this, a self-supervised domain adaptation method using generative artificial intelligence based on inpainting is proposed. This approach generates site-specific crack images and labels by fine-tuning Stable Diffusion model with DreamBooth. The resulting data set is then used to train a crack detection neural network using self-supervised learning. Evaluations across two target domain data sets and eight models show average F1-score improvements of 25.82% and 17.83%. A comprehensive tunnel ceiling field test further demonstrates the effectiveness of the method. By enhancing real-world crack detection capabilities, this approach supports better structural safety management.