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

Zhuangzhuang Chen

Abstract: Crack segmentation datasets make great efforts to ob- tain 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 re- gion, due to low contrast and heterogeneous texture. To solve this problem, we propose a novel clustering-inspired representation learning framework, which contains a two- phase 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 segmenta- tion 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 supervi- sion learning of these regions into an unsupervised clus- tering manner. We demonstrate that the proposed method could surpass the existing crack segmentation models on various datasets and our constructed CrackSeg5k dataset.