Abstract
Surface-defect detection aims to accurately locate and classify defect areas in images via pixel-level annotations. Different from the objects in traditional image segmentation, defect areas comprise a small group of pixels with random shapes, characterized by uncommon textures and edges that are inconsistent with the normal surface patterns of industrial products. This task-specific knowledge is hardly considered in the current methods. Therefore, we propose a two-stage “promotion-suppression” transformer (PST) framework, which explicitly adopts the wavelet features to guide the network to focus on the detailed features in the images. Specifically, in the promotion stage, we propose the Haar augmentation module to improve the backbone’s sensitivity to high-frequency details. However, the background noise is inevitably amplified as well because it also constitutes high-frequency information. Therefore, a quadratic feature-fusion module (QFFM) is proposed in the suppression stage, which exploits the two properties of noise, independence and attenuation. The QFFM analyzes the similarities and differences between noise and defect features to achieve noise suppression. Compared with the traditional linear-fusion approach, the QFFM is more sensitive to high-frequency details; thus, it can afford highly discriminative features. Extensive experiments are conducted on three datasets, namely DAGM, MT, and CRACK500, which demonstrate the superiority of the proposed PST framework.
