Cotton Fabric Defect Detection Based on K-SVD Dictionary Learning

Abstract

To improve the detection for different fabric types and defect kinds, an approach based on K-Singular Value Decomposition (K-SVD) dictionary learning method is designed to detect fabric defects, which remains important and challenging in the field of textile engineering. The proposed method has two main parts, namely the training and detection. The 32 defect-free fabric samples were used to train the dictionary by K-SVD. The detection process mainly consists of image segmentation, reconstruction by the trained dictionary, and detection. To improve the detection speed, the patch size was applied for detecting the defects, and the learned dictionaries were trained offline. For selecting general patch size, three patch sizes of 16 $$ \times $$ 16 26 $$ \times $$ 26, and 36 $$ \times $$ 36 were used in the experiment. A comparative study found that the 26 $$ \times $$ 26 has a comprehensive performance. The size and location of the defects were denoted by the black rectangle. Experimental results on 55 fabric samples demonstrated that the proposed method can efficiently detect different kinds of fabric defects and fabric types based on false detection rate and the correct detection rate.

Keywords

• defect detection
• sparse coding
• k-svd dictionary learning
• woven fabric
• threshold
• texture