A universal defect detection approach for various types of fabrics based on the Elo-rating algorithm of the integral image

Abstract

In order to overcome the shortcoming that a fabric defect detection method can only fit into a certain type of fabric, this paper presents a novel method by integrating the idea of the integral image into the Elo-rating algorithm (IIER), which can detect the defects of various types of fabric speedily. Firstly, the golden sub-blocks are extracted from defect-free images. The whole images are divided into many sub-blocks, and the integral images of these sub-blocks are obtained. Next, the R sub-blocks are randomly selected from these integral sub-blocks, and each block is assigned an initial Elo point. Afterwards, the R sub-blocks are matched against all sub-blocks and the Elo points are updated after each competition. Finally, regions with bright defects accumulate high Elo points and regions with dark defects accumulate low Elo points. Thus, the threshold value image can be obtained by thresholding the final Elo points, in which white, gray and black regions correspond to bright, dark-defect and defect-free regions, respectively. The performance of the proposed method is evaluated on databases of three categories of fabric, namely raw fabric, yarn-dyed fabric and patterned fabric. The experimental results show that the IIER is a universal algorithm, which has high detection rate for different types of fabrics; in particular, the average correct detection rate can reach 100% for dot-patterned fabric. In addition, the detection time can be significantly reduced comparing with the Elo-rating algorithm (ER). Particularly for star-patterned fabric, the average detection time per image is 24.18 seconds less than the ER.