Fabric defect image segmentation based on the visual attention mechanism of the wavelet domain

Abstract

In order to explore the accurate image segmentation of fabric defects, we will introduce the visual attention mechanism of the wavelet domain to the dynamic detection of fabric defects. First of all, feature maps are formed by extracting simple features from a collection image. Secondly, feature maps by multi-layer wavelet decomposition are decomposed into a lot of feature sub-maps of the wavelet domain. On this basis, the center-surround operator among feature sub-maps of the wavelet domain is adopted to build the feature difference sub-maps, which are fused into feature saliency maps through fuse strategy. Finally, the defect interest areas are segmented based on the maximum between-cluster variance method in saliency maps, and the fabric defects through the region growing method are detected in the defect interest areas. Comparing with the wavelet transform algorithm, experimental results show that the proposed method is able to segment the defect information completely, and it has a strong ability to resist noise interference, which can improve the accuracy of defect detection.