Defect attention template generation cycleGAN for weakly supervised surface defect segmentation

Highlights：

• A novel defect attention module is proposed in the discriminator of Cycle-GAN to better eliminate defects with weak signals, such as small region and low contrast, for more accurate template images.

• A defect consistency loss is proposed by adding SSIM to the L1 loss based on grayscale and texture, respectively, for better modeling the inner structure of defects.

• The proposed method has been successfully applied at an industrial site for commutator surface defect detection and has exhibited excellent detection accuracy and significantly reduced manual labeling costs.

• The proposed method can also be employed as a semiautomatic annotation tool combined with active learning.

摘要

•A weakly supervised defect segmentation method that does not require pixel-level annotations of defects for training is proposed based on the dynamic templates generated by an improved Cycle-GAN, which can achieve significantly higher performance than other weakly supervised methods and have competitive performance with supervised methods.•A novel defect attention module is proposed in the discriminator of Cycle-GAN to better eliminate defects with weak signals, such as small region and low contrast, for more accurate template images.•A defect consistency loss is proposed by adding SSIM to the L1 loss based on grayscale and texture, respectively, for better modeling the inner structure of defects.•The proposed method has been successfully applied at an industrial site for commutator surface defect detection and has exhibited excellent detection accuracy and significantly reduced manual labeling costs.•The proposed method can also be employed as a semiautomatic annotation tool combined with active learning.