Fabric Defect Detection Based on Gabor Filter and Tensor Low-Rank Recovery

Fabric defect detection plays a curial step in the quality control of textiles. Existing fabric defect detection methods are lack of adaptability and have a poor detection performance. A novel fabric defect detection method based on Gabor filter and tensor low-rank recovery was proposed in this paper. Defect-free fabric images have the specified direction, while defects damage their regularity of direction. Therefore, the direction feature is curial for fabric defect detection. For different kinds of fabric image, the direction information is also distinct. In order to characterize the direction information for all kinds of fabric image, we adopted a bank of Gabor directional filters to extract directional information, and generated the directional Gabor filtered maps. Thereafter, an efficient TRPCA model is proposed to decompose the feature tensor which is generated by stacking the feature vector of all the feature maps into a low-rank tensor and a sparse tensor by the alternating direction method of multipliers according to the tensor recovery (ADMM-TR) techniques. Finally, the saliency map generated by the sparse tensor part is segmented via the improved adaptive thresholding algorithm to locate the defective regions. Experimental results demonstrate that our algorithm is superior to the state-of-the-art.