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Surface defect inspection is of great importance for industrial manufactureand production. Though defect inspection methods based on deep learning havemade significant progress, there are still some challenges for these methods,such as indistinguishable weak defects and defect-like interference in thebackground. To address these issues, we propose a transformer network withmulti-stage CNN (Convolutional Neural Network) feature injection for surfacedefect segmentation, which is a UNet-like structure named CINFormer. CINFormerpresents a simple yet effective feature integration mechanism that injects themulti-level CNN features of the input image into different stages of thetransformer network in the encoder. This can maintain the merit of CNNcapturing detailed features and that of transformer depressing noises in thebackground, which facilitates accurate defect detection. In addition, CINFormerpresents a Top-K self-attention module to focus on tokens with more importantinformation about the defects, so as to further reduce the impact of theredundant background. Extensive experiments conducted on the surface defectdatasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormerachieves state-of-the-art performance in defect detection.

CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation