Large-scale foundation models have become the mainstream method in the field of deep learning, while in civil engineering, the scale of AI models is strictly limited. In this work, vision foundation model is introduced for crack segmentation. Two Parameter-efficient fine-tuning methods, adapter and low-rank adaptation, are adopted to fine-tune the foundation model in the field of semantic segmentation: Segment Anything Model (SAM). The fine-tuned model CrackSAM is much larger than all the existing crack segmentation models, but shows excellent performance. To test the zero-shot performance of the proposed method, two unique datasets related to road and exterior wall cracks are collected, annotated and open-sourced, in total 810 images. Comparative experiments are conducted with twelve mature semantic segmentation models. On datasets with artificial noise and previously unseen datasets, the performance of CrackSAM far exceeds that of all state-of-the-art models. CrackSAM exhibits remarkable superiority, particularly in challenging conditions such as dim lighting, shadows, road markings, construction joints, and other interference factors. Such cross-scenario results demonstrate the outstanding zero-shot capability of foundation models, and provide new ideas for the development of vision models in civil engineering.

翻译：大规模基础模型已成为深度学习领域的主流方法，但在土木工程中，AI模型的规模受到严格限制。本研究将视觉基础模型引入裂缝分割任务，采用适配器与低秩适应两种参数高效微调方法，对语义分割领域的基础模型——Segment Anything Model (SAM)进行微调。微调后的模型CrackSAM远大于现有所有裂缝分割模型，却展现出卓越性能。为测试所提方法的零样本能力，我们收集、标注并开源了两个分别涉及道路和外墙裂缝的独特数据集，共计810张图像。与十二个成熟语义分割模型进行的对比实验表明：在含人工噪声的数据集及未见过的数据集上，CrackSAM的性能远超所有最新模型。尤其在弱光、阴影、道路标线、施工缝等干扰因素构成的复杂环境中，CrackSAM展现出显著优势。这种跨场景结果证明了基础模型卓越的零样本能力，为土木工程视觉模型的发展提供了新思路。