Crack segmentation plays a crucial role in ensuring the structural integrity and seismic safety of civil structures. However, existing crack segmentation algorithms encounter challenges in maintaining accuracy with domain shifts across datasets. To address this issue, we propose a novel deep network that employs incremental training with unsupervised domain adaptation (UDA) using adversarial learning, without a significant drop in accuracy in the source domain. Our approach leverages an encoder-decoder architecture, consisting of both domain-invariant and domain-specific parameters. The encoder learns shared crack features across all domains, ensuring robustness to domain variations. Simultaneously, the decoder's domain-specific parameters capture domain-specific features unique to each domain. By combining these components, our model achieves improved crack segmentation performance. Furthermore, we introduce BuildCrack, a new crack dataset comparable to sub-datasets of the well-established CrackSeg9K dataset in terms of image count and crack percentage. We evaluate our proposed approach against state-of-the-art UDA methods using different sub-datasets of CrackSeg9K and our custom dataset. Our experimental results demonstrate a significant improvement in crack segmentation accuracy and generalization across target domains compared to other UDA methods - specifically, an improvement of 0.65 and 2.7 mIoU on source and target domains respectively.

翻译：裂缝分割对于确保土木结构的结构完整性与抗震安全至关重要。然而，现有裂缝分割算法在面对跨数据集的域偏移时难以保持准确性。为解决这一问题，本文提出一种新颖的深度网络，该网络采用基于对抗学习的增量式无监督域自适应训练方法，且能在源域上保持精度无明显下降。我们的方法采用编码器-解码器架构，其中包含域不变参数与域特定参数。编码器学习所有域共享的裂缝特征，确保对域变化的鲁棒性；同时解码器的域特定参数捕获各域独有的特征。通过结合这些组件，我们的模型实现了更优的裂缝分割性能。此外，我们构建了BuildCrack——一个新的裂缝数据集，其在图像数量与裂缝占比方面均与权威数据集CrackSeg9K的子数据集具有可比性。我们使用CrackSeg9K的不同子数据集及自建数据集，将所提方法与当前最先进的无监督域自适应方法进行对比评估。实验结果表明，相较于其他无监督域自适应方法，我们的方法在目标域的裂缝分割精度与泛化能力方面均有显著提升——具体而言，在源域与目标域上分别实现了0.65和2.7 mIoU的性能提升。