  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.
