The week's pick
Random Articles
Reseach Article
Defect Classification in Fabric Web Material using LabVIEW
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 63 - Number 18 |
| Year of Publication: 2013 |
| Authors: G. Revathy, P. Vidhyalakshmi |
10.5120/10570-5665
|
G. Revathy, P. Vidhyalakshmi . Defect Classification in Fabric Web Material using LabVIEW. International Journal of Computer Applications. 63, 18 ( February 2013), 44-48. DOI=10.5120/10570-5665
Abstract
Textile manufacturing is a major industry in India. It is based on the conversion of three types of fibre into yarn which in turn is woven into fabrics. Fabrics are textile materials which are made through weaving ,knitting, braiding and bonding of fibres. Weaving is described as inter-lacing of two distinct set of threads to form cloth, rug or other type of woven textile. The lengthways threads are known as the warp and the crossway threads are known as the weft. Quality plays a vital role in fabric manufacturing. The success of a weaving mill is significantly highlighted by its success in reducing fabric defects. Fabric inspection in offline is performed manually by skilled staff with a maximum accuracy of only 60%-75%. Automated fabric inspection would seem to offer a number of potential advantages, including improved safety, reduced labour costs, and the elimination of human error. Therefore, automated visual inspection is gaining increasing importance in weaving industry. This project proposes a automated fabric inspection system with benefits of low cost and high detection rate. Four types of faults are considered for analysis. Both normal and faulty images are processed and features are extracted using Gray Level Co-occurrence Matrix (GLCM). Further fuzzy rule based classsification is done.
References
- P. Angelov, X. Zhou, F. Klawonn (2007) " Evolving Fuzzy Rule-based Classifiers" IEEE International Conference on Computational Intelligence Applications for Signal and Image Processing, April 1-5.
- Bi Mingde,Sun Zhigang(2011) " Fabric defect detection using undecimated wavelet transform" Information Technology Journal 10(9),1701-1708.
- C. -S. Cho, B. -M. Chung and M. -J. Park(2005), "Development of real- time vision-based fabric inspection system", IEEE Trans. Ind. Electron. , vol. 52, no. 4, pp. 1073-1079 .
- Emmanuel Schmitt, Vincent Bombardier, and Laurent Wendling(2008) "Improving Fuzzy Rule Classifier by Extracting Suitable Features From Capacities With Respect to the Choquet Integral" IEEE transactions on systems, man, and cybernetics—part b: cybernetics, vol. 38, no. 5.
- Henry Y. T. Ngan , Grantham K. H. Pang , Nelson H. C. Yung (2011) "Automated fabric defect detection-A review" Image and Vision Computing 29, 442–458.
- Henry Y. T. Ngan, Grantham K. H. Pana, S. P. Yung, Michael K. Ng(2005), "Wavelet based methods on patterned fabric defect detection"Pattern Recognition 38 559 – 576.
- Kumar A. , (2008) "Computer-Vision-Based Fabric Defect Detection: A Survey", IEEE Trans. of Industrial Electronics, vol. 55,No. 1, pp. 348-363.
- Li-Wei Han De Xu(2010) "Statistic Learning-based Defect Detection for Twill Fabrics" International Journal of Automation and Computing 7(1), 86-94.
- Mahajan P. M. , Kolhe S. R. And Patil P. M. , (2009) "A review of automatic fabric defect detection techniques" Advances in Computational Research, Volume 1,Issue 2,pp-18-29.
- Tzung-Pei Hong, Chai-Ying Lee (1996) "Induction of fuzzy rules and membership functions from training examples"Fuzzy Sets and Systems 84 33 -47.
- Yu Zhang, Zhaoyang Lu,Jing Li(2010) "Fabric defect classification using radial basis function network" Pattern Recognition Letters 31, 2033–2042
- Yanfang Han , Pengfei Shi (2007) "An adaptive level-selecting wavelet transform for texture defect detection" Image and Vision Computing 25, 1239–1248.
Index Terms
Keywords