Optimal Gabor Filtering for the Inspection of Striped Fabric

As an important part of products’ quality control, automatic fabric inspection has attracted much attention in the past. Compared with manual inspection, automatic inspection can achieve not only more accurate detection results but also a higher efficiency. With the diversified fabric texture and patterns, it is very necessary to develop distinctive detection methods for different types of fabric. In this paper, based on optimal Gabor filters, a novel defect detection model is proposed to address the inspection of striped fabric, which is commonly used in our daily dresses. In the framework of the detection model, Gabor filters perpendicular to the stripe pattern are optimized to minimize the variance of the image but enhance the features of defects. Thereafter, an adaptive thresholding is set to accurately segment the defective image area. The evaluation of the proposed detection model is conducted using samples of the TILDA database. It is revealed that the common fabric defects as well as the pattern variants could be successfully detected through the proposed detection model.