NCLRNet: A Shallow Network and Non-convex Low-Rank Based Fabric Defect Detection

Abstract

Fabric images have complex and regular texture features, and defects destroy this regularity, which can be considered as sparse parts in background. Low rank representation technique has been proven applicable in fabric defect detection, which decomposes fabric image into sparse parts and redundant background. Traditional low-rank representation model is resolved by convex surrogate, which results in an inaccurate solution. In addition, the performance of low-rank representation model relies on the characterization capabilities of feature descriptor. But the hand-crafted features cannot effectively describe the complex fabric texture. To solve these issues, we propose a fabric defect detection algorithm based on a shallow network and Non-convex Low rank representation (NCLRNet). In this process, we design a shallow convolutional neural network to improve the efficiency of feature extraction, and the non-convex method is introduced into the low rank representation model to get the accurate solution. Moreover, the detection results of different feature layers are fused together by the double low rank matrix representation algorithm to achieve a better detection performance. Experimental results on fabric images demonstrate the effectiveness and robustness of our proposed method.