Journal Title : International Journal of Modern Trends in Engineering and Science
Paper Title : AUTOMATIC FABRIC DEFECT DETECTION USING FUZZY C MEANS SEGMENTATION AND CLASSIFY BASED ON FCSDA
Volume 03 Issue 11 2016
ISSN no: 2348-3121
Page no: 51-54
Abstract – In textile industry production, automatic fabric inspection is important for maintain the fabric quality. For a long time the fabric defects inspection process is still carried out with human visual inspection, and thus, insufficient and costly. Therefore, automatic fabric defect inspection is required to reduce the cost and time waste caused by defects. The development of fully automated web inspection system requires robust and efficient fabric defect detection algorithms. In this paper we propose a Fisher Criterion Based Stacked Denoising Auto encoder for defect detection. The first method is divide the fabric patches according to weight of both defective and defect less patches. Then fabrics are classified into defected and defect less. Finally the defected part of the fabric region is segmented through proposed fuzzy c means segmentation.
Keywords— Deep Learning, Denoising Autoencoder (DA), Fabric Defect Detection, Fisher Criterion, FuzzyCMeans
- Yundong Li, Weigang Zhao, and Jiahao Pan”Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning” IEEE Transactions on Automation Science and Engineering .
- H. Y. T. Ngan, G. K. H. Pang, and N. H. C. Yung, “Automated fabric defect detection-A review,” Image Vision Comput., vol. 29, no. 7, pp. 442–458, 2011.
- D. Schneider and D. Merh, “Blind weave detection for woven fabrics,” Pattern Anal. Appl., vol. 18, no. 3, pp. 725–737, 2015.
- G. Hu, Q. Zhang, and G. Zhang, “Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage,” Appl. Opt., vol. 54, no. 10, pp. 2963–2980, 2015.
- E. Mohamed, H. Mounir, Q. Khadijah, and S. Ebraheem, “Application of principal component analysis to boost the performance of an auto-mated fabric fault detector and classifier,” Fibres Textiles in Eastern Europe, vol. 22, no. 4, pp. 51–57, 2014.
- M. As, J. Y. Drean, L. Bigue, and J. F. Osselin, “Optimization of au-tomated online fabric inspection by fast Fourier transform (FFT) and cross-correlation,” Textile Res. J., vol. 83, no. 3, pp. 256–268, 2013.
- B. Zhu, J. Liu, R. Pan, W. Gao, and J. L. Liu, “Seam detection of in-homogeneously textured fabrics based on wavelet transform,” Textile Res. J., vol. 85, no. 13, pp. 1381–1393, 2015.
- P. F. Li, H. H. Zhang, J. F. Jing, R. Z. Li, and J. Zhao, “Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method,” J. Textile Inst., vol. 106, no. 6, pp. 587–592, 2015.
- G. H. Hu, G. H. Zhang, and Q. H. Wang, “Automated defect detection in textured materials using wavelet-domain hidden Markov models,” Opt. Eng., vol. 53, no. 9, 2014.
- Z. J. Wen, J. J. Cao,X. P. Liu, and S. H. Ying, “Fabric defects detection using adaptive wavelets,” Int. J. Clothing Sci. Technol., vol. 26, no. 3, pp. 202–211, 2014.