Unsupervised patterned fabric defect detection using texture filtering and K-means clustering

This paper introduces an unsupervised fabric defect detection algorithm that does not need any user-adaptation and reveals high detection accuracy rates at the same time. This algorithm is mainly applied to patterned fabrics and can be extended to plain fabric. To achieve this, firstly, non-extensive standard deviation filtering is used with a suitable window size. Secondly, the image is split into blocks with size equals the periodic pattern of the fabric image. Thirdly, the square difference between each block median and the mean of all block medians are calculated. Finally, all these differences are forwarded to K-means clustering to classify the blocks into defected and non-defected ones. The proposed algorithm showed promising performance with overall detection success rate that reaches 95%.