Abstract
In textile industry production, fabric defect inspection is a very important step to ensure the quality of fabric. At present, most of the methods can detect the defects for solid color with the distinguishable defects, but they are not very efficient for small defects, especially for the defects which has small difference with the background. In this paper, we propose a three-phase method, mean filter, convolution operator combined with variance (MCV), for fabric defect detection. For a fabric image, we first use mean filter to suppress noise, then convolution operator is applied to enhance image. Based on enhanced image, we divide it into many patches. For a given patch, we calculate its variance and then use the threshoding to decide whether the patch is free defect or not. Finally, a defect image will be synthesized from these processed patches. Experimental results prove the effectiveness of the proposed MCV algorithm.
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Sari-Sarraf, H., Goddard, J.S.: Vision systems for on-loom fabric inspection. IEEE Trans. Ind. Appl. 35(6), 1252–1259 (1999)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans Syst. Man Cybern. 3(6), 610–621 (1973)
Tsai, I.S., Lin, C.H., Lin, J.J.: Applying an artificial neural network to pattern recognition in fabric defects. Text. Res. J. 65(3), 123–130 (1995)
Latif-Amet, A., Ertuzun, A., Ercil, A.: An efficient method for texture defect detection: sub-band domain co-occurrence matrices. Image Vis. Comput. 18, 543–555 (2000)
Zhang, Y.F., Bresee, R.R.: Fabric defect detection and classification using image analysis. Text. Res. J. 65(1), 1–9 (1995)
Bu, H.-G., Wang, J., Huang, X.-B.: Fabric defect detection based on multiple fractal features and support vector data description. Eng. Appl. Artif. Intell. 22(2), 224–235 (2009)
Chan, C.H., Pang, G.: Fabric defect detection by Fourier analysis. IEEE Trans. Ind. Appl. 36(5), 1743–1750 (2000)
Murtagh, F.D.: Automatic visual inspection of woven textiles using a two-stage defect detector. Opt. Eng. 37(37), 2536–2542 (1998)
Tsai, I.S., Hu, M.C.: Automated inspection of fabric defects using an artificial neural networks. Text. Res. J. 66, 474–482 (1996)
Tsai, D.-M., Heish, C.-Y.: Automated surface inspection for directional textures. Image Vis. Comput. 18, 49–62 (1999)
Yang, X.Z., Pang, G.K.H., Yung, N.H.C.: Discriminative fabric defect detection using adaptive wavelets. Opt. Eng. 41(41), 3116–3126 (2002)
Han, Y., Shi, P.: An adaptive level-selecting wavelet transform for texture defect detection. Image Vis. Comput. 25(8), 1239–1248 (2007)
Tsai, D.M., Hsiao, B.: Automatic surface inspection using wavelet reconstruction. Pattern Recogn. 34, 1285–1305 (2001)
Tsai, D.M., Chiang, C.H.: Automatic band selection for wavelet reconstruction in the application of defect detection. Image Vis. Comput. 21, 413–431 (2003)
Kumar, A., Pang, G.K.H.: Defect detection in textured materials using Gabor filters. IEEE Trans. Ind. Appl. 38(2), 425–440 (2002)
Zhang, Yu., Lu, Z., Li, J.: Fabric defect detection and classification using Gabor filters and gaussian mixture model. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5995, pp. 635–644. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12304-7_60
Tong, L., Wong, W.K., Kwong, C.K.: Differential evolution-based optimal Gabor filter model for fabric inspection. Neurocomputing 173, 1386–1401 (2016)
Zhou, J., Semenovich, D., Sowmya, A., Wang, J.: Dictionary learning framework for fabric defect detection. J. Text. Inst. 105(3), 223–234 (2014)
Alata, O., Ramananjarasoa, C.: Unsupervised textured image segmentation using 2-D quarter plan autoregressive model with four prediction supports. Pat. Rec. Lett. 26(8), 1069–1081 (2005)
Ozdemir, S., Ercil, A.: Markov random fields and Karhunen-Loeve transform for defect inspection of textile products. In: IEEE Conference on Emerging Technologies & Factory Automation, pp. 697–703 (1996)
Aharon, M., Elad, M., Bruckstein, A.M.: K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006)
Hu, G.H., Wang, Q.H., Zhang, G.H.: Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage. Appl. Opt. 54, 2963–2980 (2015)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 61601235, 61602252, in part by the Natural Science Foundation of Jiangsu Province of China under Grants BK20160972, BK20170768, BK20160967, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grants 16 KJB520031, 17KJB520019, 16KJB510024, 17KJB520021, in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology (NUIST) under Grant 2243141601019.
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Jiang, J., Cui, Y., Jin, Z., Fan, C. (2018). Fast Three-Phase Fabric Defect Detection. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11066. Springer, Cham. https://doi.org/10.1007/978-3-030-00015-8_26
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DOI: https://doi.org/10.1007/978-3-030-00015-8_26
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