Mind Marginal Non-Crack Regions: Clustering-Inspired Representation Learning for Crack Segmentation
Authors: Zhuangzhuang Chen, Zhuonan Lai, Jie Chen, Jianqiang Li

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 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 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.