Global Fabric Defect Detection Based on Unsupervised Characterization

Abstract

Fabric texture intelligent analysis comprises the following characteristics: objective detection results, high detection efficiency, and accuracy. It is significantly vital to replace manual inspection for smart green manufacturing in the textile industry, such as quality control and rating, and online testing. For detecting the global image, an unsupervised method is proposed to characterize the woven fabric texture image, which is the combination of principal component analysis (PCA) and dictionary learning. First of all, the PCA approach is used to reduce the dimension of fabric samples, the obtained eigenvector is used as the initial dictionary, and then the dictionary learning method is operated on the defect-free region to get the standard templates. Secondly, the standard templates are optimized by choosing the appropriate dictionary size to construct a fabric texture representation model that can effectively characterize the defect-free texture region, while ineffectively representing the defective sector. That is to say, through the mechanism of identifying normal texture from imperfect texture, a learned dictionary with robustness and discrimination is obtained to adapt the fabric texture. Thirdly, after matching the detected image with the standard templates, the average filter is used to remove the noise and suppress the background texture, while retaining and enhancing the defect region. In the final part, the image segmentation is operated to identify the defect. The experimental results show that the proposed algorithm can adequately inspect fabrics with defects such as holes, oil stains, skipping, other defective types, and non-defective materials, while the detection results are good and the algorithm can be operated flexibly.