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Sensors  2013 

Online Fabric Defect Inspection Using Smart Visual Sensors

DOI: 10.3390/s130404659

Keywords: machine vision, fabric defect inspection, smart visual sensor, wavelet transform, mathematical morphology filter

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Fabric defect inspection is necessary and essential for quality control in the textile industry. Traditionally, fabric inspection to assure textile quality is done by humans, however, in the past years, researchers have paid attention to PC-based automatic inspection systems to improve the detection efficiency. This paper proposes a novel automatic inspection scheme for the warp knitting machine using smart visual sensors. The proposed system consists of multiple smart visual sensors and a controller. Each sensor can scan 800 mm width of web, and can work independently. The following are considered in dealing with broken-end defects caused by a single yarn: first, a smart visual sensor is composed of a powerful DSP processor and a 2-megapixel high definition image sensor. Second, a wavelet transform is used to decompose fabric images, and an improved direct thresholding method based on high frequency coefficients is proposed. Third, a proper template is chosen in a mathematical morphology filter to remove noise. Fourth, a defect detection algorithm is optimized to meet real-time demands. The proposed scheme has been running for six months on a warp knitting machine in a textile factory. The actual operation shows that the system is effective, and its detection rate reaches 98%.


[1]  Abouelela, A.; Abbas, H.M.; Eldeeb, H.; Wahdan, A.A.; Nassar, S.M. Automated vision system for localizing structural defects in textile fabrics. Pattern Recognit. Lett. 2005, 26, 1435–1443.
[2]  Saeidi, M.R.G.; Latifi, M.; Najar, S.S.; Saeidi, A.G. Computer vision-aided fabric inspection system for on-circular knitting. Text. Res. J. 2005, 75, 492–497.
[3]  Furferi, R.; Governi, L. Development of an artificial vision inspection system for real-time defect detection and classification on circular knitting machines. WSEAS Trans. Comput. 2006, 5, 1186–1193.
[4]  Mak, K.L.; Peng, P. An automated inspection system for textile fabrics based on gabor filters. Robot. Comput.-Integr. Manuf. 2008, 24, 359–369.
[5]  Sun, Y.; Long, H.R. Adaptive detection of weft-knitted fabric defects based on machine vision system. J. Text. Inst. 2011, 102, 823–836.
[6]  Kumar, A. Computer-vision-based fabric defect detection: A survey. IEEE Trans. Ind. Electron. 2008, 55, 348–363.
[7]  Ngan, H.Y.T.; Pang, G.K.H.; Yung, N.H.C. Automated fabric detect detection-a review. Image Vis. Comput. 2011, 29, 442–458.
[8]  Chan, C.H.; Pang, G.K.H. Fabric defect detection by fourier analysis. IEEE Trans. Ind. Appl. 2000, 36, 1267–1276.
[9]  Ngan, H.Y.T.; Pang, G.K.H.; Yung, S.P.; Ng, M.K. Wavelet based methods on patterned fabric defect detection. Pattern Recognit. 2005, 38, 559–576.
[10]  Kumar, A.; Pang, G.K.H. Defect detection in textured materials using gabor filters. IEEE Trans. Ind. Appl. 2002, 38, 425–440.
[11]  Cui, L.L.; Lu, Z.Y.; Li, J.; Li, Y.H. Novel algorithm for automated detection of fabric defect images. J. Xidian Univ. 2011, 38, 66–72.
[12]  Ojala, T.; Pietik?inen, M.; M?enp??, T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24, 971–987.
[13]  Tajeripour, F.; Kabir, E.; Sheikhi, A. Defect detection in patterned fabrics using modified local binary patterns. Int. Conf. Comput. Intell. Multimed. Appl. 2007, 2, 263–270.
[14]  Liu, Z.F.; Gao, E.J.; Li, C. A novel fabric defect detection scheme based on improved local binary pattern operator. Int. Conf. Intell. Syst. Des. Eng. Appl. 2011, 1, 116–119.
[15]  Kwak, C.; Ventura, J.A.; Karim, T.S. Neural network approach for defect identification and classification on leather fabric. J. Intell. Manuf. 2000, 11, 485–499.
[16]  Kumar, A. Neural network based detection of local textile defects. Pattern Recognit. 2003, 36, 1645–1659.
[17]  Wong, W.K.; Yuen, C.W.M.; Fan, D.D.; Chan, L.K.; Fung, E.H.K. Stitching defect detection and classification using wavelet transform and BP neural network. Expert Syst. Appl. 2009, 36, 3845–3856.
[18]  Qing, X.Y.; Duan, H.; Wei, J.M.; Wang, L.J. A new method to inspect and recognize fabric defects based on wavelet analysis and neural network. Chin. J. Sci. Instrum. 2005, 26, 618–622.
[19]  Tsai, D.M.; Huang, T.Y. Automated surface inspection for statistical textures. Image Vis. Comput. 2003, 21, 307–323.


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