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Unsupervised defect detection algorithm for printed fabrics using content-based image retrieval techniques
- Source :
- Textile Research Journal. 91:2551-2566
- Publication Year :
- 2021
- Publisher :
- SAGE Publications, 2021.
-
Abstract
- 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.
- Subjects :
- Polymers and Plastics
Computer science
business.industry
media_common.quotation_subject
Pattern recognition
02 engineering and technology
Content-based image retrieval
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Chemical Engineering (miscellaneous)
Unsupervised learning
020201 artificial intelligence & image processing
Quality (business)
Artificial intelligence
business
media_common
Subjects
Details
- ISSN :
- 17467748 and 00405175
- Volume :
- 91
- Database :
- OpenAIRE
- Journal :
- Textile Research Journal
- Accession number :
- edsair.doi...........cdc28fe597aa0417ddff95406cd425cf
- Full Text :
- https://doi.org/10.1177/00405175211008614