UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via
  Adversarial Image Restoration
Nachuan Ma Rui Fan Lihua Xie
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 data-driven
algorithms 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
generalization ability 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 comparable 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.