Dictionary learning framework for fabric defect detection

Abstract

We present a new approach using dictionary learning framework to address textile fabric defect detection. Textile fabrics are textured materials whose images exhibit high periodicity among the repeated sub-patterns determined by weaving structure. Inspired by the image de-noising using the learned dictionary, we learn a dictionary from patches of textile fabric images, such a dictionary is able to approximate training samples well through a linear summation of its elements. Fabric defects can be regarded as a local anomaly against the relatively homogeneous texture. When modelling new samples with the dictionary learned from only the examples containing normal fabrics, the approximated version of an abnormal or defective sample will no longer contain defective region, resulting in a larger dissimilarity than a normal one, since the learned dictionary has been tuned to normal fabric structural features. Therefore, simply measuring the similarity between the original and its approximation is able to efficiently discriminate defective samples from normal, and a recently developed novelty detection algorithm, the support vector data description, is used to handle classification task. Experimental results show that the proposed algorithm can control both false alarm rate and missing detection rate within 5%, and extensions are also conducted.