UP-CrackNet：基于对抗式图像复原的无监督像素级道路裂缝检测
UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration
AI总结：
本文提出UP-CrackNet，通过随机掩码破坏无裂缝道路图像并训练生成对抗网络进行图像复原，利用复原误差实现无监督像素级裂缝检测，在性能和泛化能力上优于现有方法。
摘要：
在过去十年中，自动化方法已被开发出来，以更高效、更准确、更客观地检测裂缝，其最终目标是取代传统的人工目视检查技术。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了有前景的结果。然而，训练此类网络需要大量具有像素级标注的人工标注数据集，这是一个高度劳动密集且耗时的过程。此外，基于监督学习的方法在未见过的数据集上往往泛化能力较差。因此，我们提出了一种无监督像素级道路裂缝检测网络，称为UP-CrackNet。我们的方法首先生成多尺度方形掩码，并随机选择它们以通过移除特定区域来破坏未损坏的道路图像。随后，训练一个生成对抗网络，通过利用从周围未损坏区域学习到的语义上下文来恢复被破坏的区域。在测试阶段，通过计算输入图像与复原图像之间的差异生成误差图，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，表现出令人满意的性能和优越的泛化能力。我们的源代码在mias.group/UP-CrackNet上公开可用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
