Unsupervised defect detection algorithm for printed fabrics using content-based image retrieval techniques

Automatic detection of printing defect technology is significant for improving printing fabrics’ appearance and quality. In this research, we proposed an unsupervised printing defect detection method by processing the difference map between the test image and the reference image. Aimed at this, we adopted a content-based image retrieval (CBIR) method to retrieve the reference image, which includes an image database, a convolutional denoising auto-encoder (CDAE) and a hash encoder (HE): the elements of image database are extracted from only one defect-free sample image of the test fabric; the CDAE prevents the system being affected by the texture of the fabric and provides a reliable feature description of the patterns; the HE indexes the feature vectors to binary code while maintaining their similarity; both CDAE and HE are trained in an unsupervised manner. With the retrieved reference image, the defect is determined by applying the Tsallis entropy thresholding and opening operation on the difference map. The method can be implemented without labeled and defective samples, and without consideration of the periodical primitive of patterns. Experimental results demonstrate the effectiveness and efficiency of the proposed method in defect detection for printed fabrics with complex patterns.