Large-scale foundation models have become the mainstream deep learning method, while in civil engineering, the scale of AI models is strictly limited. In this work, a 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 semantic segmentation: the Segment Anything Model (SAM). The fine-tuned CrackSAM model 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, for a total of 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 under challenging conditions such as dim lighting, shadows, road markings, construction joints, and other interference factors. These cross-scenario results demonstrate the outstanding zero-shot capability of foundation models and provide new ideas for developing vision models in civil engineering.

翻译：大规模基础模型已成为深度学习的主流方法，然而在土木工程领域，AI模型的规模受到严格限制。本研究引入了一种用于裂缝分割的视觉基础模型，采用适配器（Adapter）和低秩适应（Low-Rank Adaptation）两种参数高效微调方法，对语义分割基础模型——Segment Anything Model（SAM）进行微调。微调后的CrackSAM模型远超所有现有裂缝分割模型的规模，但展现出卓越性能。为测试所提方法的零样本能力，我们收集、标注并开源了与道路和外墙裂缝相关的两个独特数据集，共计810张图像。与十二个成熟的语义分割模型进行了对比实验。在含人工噪声的数据集及未见过的数据集上，CrackSAM的性能显著超越所有最先进模型。尤其在光线昏暗、阴影、道路标线、施工接缝等干扰因素下的困难场景中，CrackSAM展现出显著优势。这些跨场景结果证明了基础模型卓越的零样本能力，为土木工程领域视觉模型的发展提供了新思路。