Self-Adaptive Fourier Augmentation Framework for Crack Segmentation in Industrial Scenarios

Crack segmentation receives extensive attention in structure health monitoring for many industrial scenarios, e.g., bridges, highways, and nuclear power plants. The current deep learning-based crack segmentation models enjoy the ability to extract discriminative crack features by training with an extensive labeled crack dataset. However, collecting extensive crack samples with accurate annotations from experts for a new scenario is labor-intensive, thereby limiting the effectiveness of these deep models in practical applications. To address this problem, the existing Fourier-based augmentation adopts a vanilla amplitude fusion process, i.e., the portion of amplitude components is fixed or randomly selected, failing to guarantee augmented samples’ semantics consistency, and diversity concerning the original sample. To fill this, this article proposes a self-adaptive Fourier augmentation framework that efficiently synthesizes diverse crack samples for training crack segmentation models. Our proposed framework advances Fourier transformation in an adversarial learning manner, alternating between self-adaptive Fourier-based data augmentation and teacher–student learning. The former aims to guarantee the diversity and semantics consistency of Fourier-based augmented samples, while the latter progressively updates the student network by observing these augmented samples for extracting discriminative features via a knowledge distillation mechanism. It is worth noting that the proposed method is only applied in the training stage without extra computation and memory during inference. Extensive experiments demonstrate the superiority of our method over the existing methods.