Decompose-Compose Feature Augmentation for Imbalanced Crack Recognition in Industrial Scenarios

Zhuangzhuang Chen, Chengqi Xu, Tao Hu, Jie Chen, Jianqiang Li

Abstract: Automated crack recognition has achieved remarkable progress in the past decades as a critical task in structure health monitoring, to ensure safety and durability in many industrial scenarios. However, imbalanced crack recognition remains challenging due to the scarcity of crack samples and the consequential limited diversity. To resolve this, Artificial Intelligence Generated Content (AIGC) has been gradually adopted to generate synthetic data and reduce reliance on large amounts of labeled crack samples. This paper assumes that a crack sample in the feature space can be regarded as a combination of crack and background semantics. Then, the decompose-compose feature augmentation framework (DeCo) is proposed to perform crack data synthesis in the feature space by randomly composing crack and background semantic-relevant features. Specifically, the contrastive learning-based decomposing loss is proposed to enforce two encoders to separately learn crack and background semantics from crack samples with the theoretical guarantee. After that, an effective cross-instance feature union strategy is proposed to synthesize diverse crack samples by composing the crack-relevant features from a crack sample and background-relevant features across other training samples. To address the limited availability of related benchmarks, we collect INPP2022 and IRC2022 datasets from real-world applications in nuclear power plants and road pavement. Experimental results show that DeCo performs favorably against state-of-the-art competitors in imbalanced crack recognition tasks.