Over the past decade, automated methods have been developed to detect cracks more efficiently, accurately, and objectively, with the ultimate goal of replacing conventional manual visual inspection techniques. Among these methods, semantic segmentation algorithms have demonstrated promising results in pixel-wise crack detection tasks. However, training such networks requires a large amount of human-annotated datasets with pixel-level annotations, which is a highly labor-intensive and time-consuming process. Moreover, supervised learning-based methods often struggle with poor generalizability in unseen datasets. Therefore, we propose an unsupervised pixel-wise road crack detection network, known as UP-CrackNet. Our approach first generates multi-scale square masks and randomly selects them to corrupt undamaged road images by removing certain regions. Subsequently, a generative adversarial network is trained to restore the corrupted regions by leveraging the semantic context learned from surrounding uncorrupted regions. During the testing phase, an error map is generated by calculating the difference between the input and restored images, which allows for pixel-wise crack detection. Our comprehensive experimental results demonstrate that UP-CrackNet outperforms other general-purpose unsupervised anomaly detection algorithms, and exhibits satisfactory performance and superior generalizability when compared with state-of-the-art supervised crack segmentation algorithms. Our source code is publicly available at mias.group/UP-CrackNet.

翻译：过去十年间，自动化方法被开发用于更高效、准确且客观地检测裂缝，其最终目标是取代传统的人工视觉检测技术。在这些方法中，语义分割算法在像素级裂缝检测任务中展现出良好的效果。然而，训练此类网络需要大量带有像素级标注的人工标注数据集，这一过程极其耗时费力。此外，基于监督学习的方法往往在未见数据集上泛化能力较差。为此，我们提出了一种名为UP-CrackNet的无监督像素级道路裂缝检测网络。该方法首先生成多尺度方形掩膜，并随机选取它们通过移除部分区域来破坏完好的道路图像。随后，利用生成对抗网络，通过从周围未损坏区域学习语义上下文来修复被破坏区域。在测试阶段，通过计算输入图像与修复图像的差异生成误差图，从而实现像素级裂缝检测。我们的综合实验结果表明，UP-CrackNet优于其他通用无监督异常检测算法，并且与最先进的监督裂缝分割算法相比，展现出令人满意的性能和卓越的泛化能力。我们的源代码已公开于mias.group/UP-CrackNet。