The reliable and fast detection of cracks is crucial for assessing the condition and maintaining civil infrastructure. However, due to diverse construction materials, imaging conditions, and environmental interference, there exists a domain shift between crack images collected from civil infrastructure. This shift results in significant performance drops of crack detection models trained on one dataset when applied to another, limiting their cross-dataset applicability. To address this issue, this paper proposes DACrack, an unsupervised domain adaptation framework for crack detection of civil infrastructure. The proposed method performs domain adaptation at the input, feature, and output levels using contrastive mechanisms, adversarial learning, and variational autoencoders. Extensive experiments demonstrate the effectiveness and robustness of the proposed method for cross-dataset crack detection. By mitigating the impact of domain shift, DACrack offers a more reliable and accurate solution for assessing the condition of civil infrastructure.

可靠且快速地检测裂缝对于评估状况和维护民用基础设施至关重要。然而，由于建筑材料、成像条件和环境干扰的不同，从民用基础设施中采集的裂缝图像之间存在域转移。这种转变导致在一个数据集上训练的裂缝检测模型在应用于另一个数据集时性能显着下降，从而限制了它们的跨数据集适用性。为了解决这个问题，本文提出了 DACrack，这是一种用于民用基础设施裂缝检测的无监督域自适应框架。所提出的方法使用对比机制、对抗性学习和变分自动编码器在输入、特征和输出级别执行域自适应。大量实验证明了所提出的跨数据集裂缝检测方法的有效性和鲁棒性。通过减轻域转移的影响，DACrack 为评估​​民用基础设施的状况提供了更可靠和准确的解决方案。