Deep learning-based pavement cracks detection methods often require large-scale labels with detailed crack location information to learn accurate predictions. In practice, however, crack locations are very difficult to be manually annotated due to various visual patterns of pavement crack. In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available. Specifically, DDACDN first extracts crack features from both the source and target domain by a two-branch weights-shared backbone network. And in an effort to achieve the cross-domain adaptation, an intermediate domain is constructed by aggregating the three-scale features from the feature space of each domain to adapt the crack features from the source domain to the target domain. Finally, the network involves the knowledge of both domains and is trained to recognize and localize pavement cracks. To facilitate accurate training and validation for domain adaptation, we use two challenging pavement crack datasets CQU-BPDD and RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement Multi-label Disease Dataset named CQU-BPMDD, which contains 38994 high-resolution pavement disease images to further evaluate the robustness of our model. Extensive experiments demonstrate that DDACDN outperforms state-of-the-art pavement crack detection methods in predicting the crack location on the target domain.

翻译：基于深度学习的方法在路面裂缝检测中通常需要大量带有详细裂缝位置信息的标注数据来学习精确预测。然而在实际应用中，由于路面裂缝视觉模式的多样性，人工标注裂缝位置非常困难。本文提出一种基于深度域适应的裂缝检测网络（DDACDN），该网络利用源域知识学习域不变特征，以预测仅具有图像级标签的目标域中的多类别裂缝位置信息。具体而言，DDACDN首先通过双分支权重共享骨干网络从源域和目标域提取裂缝特征。为实现跨域适应，通过聚合各域特征空间中的三尺度特征构建中间域，使源域裂缝特征适应目标域。最终该网络融合两域知识，完成路面裂缝的识别与定位训练。为促进域适应的精确训练与验证，我们使用两个具有挑战性的路面裂缝数据集CQU-BPDD和RDD2020。此外，我们构建了名为CQU-BPMDD的新型大规模沥青路面多标签病害数据集，包含38994张高分辨率路面病害图像，以进一步评估模型的鲁棒性。大量实验表明，DDACDN在目标域裂缝位置预测方面优于最先进的路面裂缝检测方法。