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 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 clus-tering manner. We demonstrate that the proposed method could surpass the existing crack segmentation models on various datasets and our constructed CrackSeg5k dataset.

Cracks are usually curve-like structures that are the focus of many computer-vision applications (e.g., road safety inspection and surface inspection of the industrial facilities). The existing pixel-based crack segmentation methods rely on time-consuming and costly pixel-level annotations. And the object-based crack detection methods exploit the horizontal box to detect the crack without considering crack orientation, resulting in scale variation and intra-class variation. Considering this, we provide a new perspective for crack detection that models the cracks as a series of sub-cracks with the corresponding orientation. However, the vanilla adaptation of the existing oriented object detection methods to the crack detection tasks will result in limited performance, due to the boundary discontinuity issue and the ambiguities in sub-crack orientation. In this paper, we propose a first-of-its-kind oriented sub-crack detector, dubbed as CrackDet, which is derived from a novel piecewise angle definition, to ease the boundary discontinuity problem. And then, we propose a multi-branch angle regression loss for learning sub-crack orientation and variance together. Since there are no related benchmarks, we construct three fully annotated datasets, namely, ORC, ONPP, and OCCSD, which involve various cracks in road pavement and industrial facilities. Experiments show that our approach outperforms state-of-the-art crack detectors.

Despite the substantial progress of deep models for crack recognition, due to the inconsistent cracks in varying sizes, shapes, and noisy background textures, there still lacks the discriminative power of the deeply learned features when supervised by the cross-entropy loss. In this paper, we propose the geometry-aware guided loss (GAGL) that enhances the discrimination ability and is only applied in the training stage without extra computation and memory during inference. The GAGL consists of the feature-based geometry-aware projected gradient descent method (FGA-PGD) that approximates the geometric distances of the features to the class boundaries, and the geometry-aware update rule that learns an anchor of each class as the approximation of the feature expected to have the largest geometric distance to the corresponding class boundary. Then the discriminative power can be enhanced by minimizing the distances between the features and their corresponding class anchors in the feature space. To address the limited availability of related benchmarks, we collect a fully annotated dataset, namely, NPP2021, which involves inconsistent cracks and noisy backgrounds in real-world nuclear power plants. Our proposed GAGL outperforms the state of the arts on various benchmark datasets including CRACK2019, SDNET2018, and our NPP2021.