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.

中文翻译：

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使用微调修复生成模型进行外推裂纹分割的自监督域自适应方法

老化基础设施的数量和比例正在增加，因此需要进行准确的检查以确保安全性和结构稳定性。虽然计算机视觉和深度学习已广泛应用于混凝土裂缝，但域偏移问题通常会导致预训练模型在新站点的性能不佳。为了解决这个问题，提出了一种基于修复的基于生成人工智能的自监督领域适应方法。这种方法通过使用 DreamBooth 微调 Stable Diffusion 模型来生成特定于位置的裂缝图像和标签。然后，使用生成的数据集通过自监督学习来训练裂纹检测神经网络。对两个目标领域数据集和八个模型的评估显示，平均 F1 分数提高了 25.82% 和 17.83%。全面的隧道吊顶现场测试进一步证明了该方法的有效性。通过增强真实世界的裂纹检测能力，这种方法支持更好的结构安全管理。