DIFFERENTIAL ENHANCED SIAMESE SEGMENTATION NETWORK FOR PRINTED LABEL DEFECT DETECTION

Many vision-based methods have been widely used to detect defects in industrial printed labels. However, most of them still face challenges of detecting unseen defects, low-contrast defects, and false detections caused by artifacts. To address these problems, we propose a differential enhanced Siamese segmentation network (DESS-Net) for defect detection. This method is based on Siamese similarity comparison which has a better generalization ability for unseen defects. Moreover, we introduce the differential feature enhancement (DFE) modules into the Siamese network to focus on multiple differential feature information which contributes to identifying defects and reducing false detections caused by artifacts. Additionally, a multi-scale feature fusion (MFF) module is further designed to fuse multiple low-level differential features, which is conducive to recovering fine boundaries of low-contrast defects. Experimental results show that our DESS-Net outperforms other compared methods.