Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning
Top Cited Papers
- 3 February 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automation Science and Engineering
- Vol. 14 (2), 1256-1264
- https://doi.org/10.1109/tase.2016.2520955
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.Keywords
Funding Information
- Beijing Education Committee science and technology project
This publication has 24 references indexed in Scilit:
- Automated defect detection in uniform and structured fabrics using Gabor filters and PCAJournal of Visual Communication and Image Representation, 2013
- Development of a machine vision system: real-time fabric defect detection and classification with neural networksThe Journal of the Textile Institute, 2013
- Supervised defect detection on textile fabrics via optimal Gabor filterJournal of Industrial Textiles, 2013
- Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domainTextile Research Journal, 2013
- Online Fabric Defect Inspection Using Smart Visual SensorsSensors, 2013
- Optimization of automated online fabric inspection by fast Fourier transform (FFT) and cross-correlationTextile Research Journal, 2012
- Automated fabric defect detection—A reviewImage and Vision Computing, 2011
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006
- Machine vision using image data feedback for fault detection in complex deformable websTransactions of the Institute of Measurement and Control, 2004