geometry-aware guided loss for deep crack recognition

Zhuangzhuang Chen, Jin Zhang, Zhuonan Lai, Jie Chen, Zun Liu, Jianqiang Li

Abstract:

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 geometryaware 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.