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Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning.

Authors :
Li, Yundong
Zhao, Weigang
Pan, Jiahao
Source :
IEEE Transactions on Automation Science & Engineering. Apr2017, Vol. 14 Issue 2, p1256-1264. 9p.
Publication Year :
2017

Abstract

In this paper, we propose a discriminative representation for patterned fabric defect detection when only limited negative samples are available. Fabric patches are efficiently classified into defectless and defective categories by Fisher criterion-based stacked denoising autoencoders (FCSDA). First, fabric images are divided into patches of the same size, and both defective and defectless samples are utilized to train FCSDA. Second, test patches are classified through FCSDA into defective and defectless categories. Finally, the residual between the reconstructed image and defective patch is calculated, and the defect is located by thresholding. Experimental results demonstrate the effectiveness of the proposed scheme in the defect detection for periodic patterned fabric and more complex jacquard warp-knitted fabric. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15455955
Volume :
14
Issue :
2
Database :
Academic Search Index
Journal :
IEEE Transactions on Automation Science & Engineering
Publication Type :
Academic Journal
Accession number :
122420365
Full Text :
https://doi.org/10.1109/TASE.2016.2520955