Mind marginal non-crack regions: Clustering-inspired representation learning for crack segmentation

Crack segmentation datasets make great efforts to obtain the ground truth crack or non-crack labels as clearly as possible. However, it can be observed that ambiguities are still inevitable when considering the marginal non-crack region, due to low contrast and heterogeneous texture. To solve this problem, we propose a novel clustering-inspired representation learning framework, which contains a twophase strategy for automatic crack segmentation. In the first phase, a pre-process is proposed to localize the marginal non-crack region. Then, we propose an ambiguity-aware segmentation loss (Aseg Loss) that enables crack segmentation models to capture ambiguities in the above regions via learning segmentation variance, which allows us to further localize ambiguous regions. In the second phase, to learn the discriminative features of the above regions, we propose a clustering-inspired loss (CI Loss) that alters the supervision learning of these regions into an unsupervised clustering manner. We demonstrate that the proposed method could surpass the existing crack segmentation models on various datasets and our constructed CrackSeg5k dataset.