Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

• Autorzy: Zhu D. , Pan R. , Gao W. , Zhang J.

Identyfikatory

DOI

10.1515/aut-2015-0001

• Języki publikacji: EN

Abstrakty

EN

In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes.

Słowa kluczowe

EN

autocorrelation function; defect detection; Euclidean distance; GLCM; yarn-dyed fabric

PL

funkcja autokorelacji; wykrywanie defektów; odległość euklidesowa

• Wydawca: Lodz University of Technology, Faculty of Material Technologies and Textile Design
• Czasopismo: Autex Research Journal
• Rocznik: 2015
• Tom: Vol. 15, no. 3
• Strony: 226--232
• Opis fizyczny: Bibliogr. 19 poz.

Twórcy

autor

Zhu D.

• School of Clothing and Textile, Jiangnan University, Wuxi, 214122, China

autor

Pan R.

• School of Clothing and Textile, Jiangnan University, Wuxi, 214122, China

autor

Gao W.

• School of Clothing and Textile, Jiangnan University, Wuxi, 214122, China

autor

Zhang J.

• School of Clothing and Textile, Jiangnan University, Wuxi, 214122, China

Bibliografia

• [1] Ngan H Y T, Pang G K H and Yung S P, (2011). Automated fabric defect detection — A review, Image and Vision Computing, 29 (7):442–458.
• [2] Kumar A, (2008). Computer-Vision-Based Fabric Defect Detection: A Survey, IEEE transactions on industrial electronics, 55(1): 348-363
• [3] Chan C H & Pang G K H, (2000) Fabric Defect Detection by Fourier Analysis. IEEE Transactions on Industry Applications, 36(5):1267-1276.
• [4] Kumar A and Pang G, (2000). Fabric defect segmentation using multichannel blob detectors, Optical Engineering, 39(12) 3176 – 3190.
• [5] Li W Y, Xue W L and CHENG L D, (2012). Intelligent detection of defects of yarn-dyed fabrics by energy-based local binary patterns, Textile Research Journal, 82(19):1960–1972.
• [6] Ngan H Y T, Pang G K H and Yung S P, (2005) Wavelet based methods on patterned fabric defect detection. Pattern Recognition Letters, 38: 559-576.
• [7] Asha, V, Bhajantri N.U and Nagabhushan, P, (2011). GLCM based Chi-square Histogram Distance for Automatic Detection of Defects on Patterned Textures. International Journal of Computational Vision and Robotics, 2(4):302-313.
• [8] Haralick, R M, (1973). Textural Features for Image Classification, IEEE TRANS. SYST., MAN, CYBERN., 3(6):610-621.
• [9] Pan R R, Gao W D, Qian X X, et al, (2010). Automatic analysis of woven fabric weave based on notation, Chinese textile research, 31(11):30-34.
• [10] Lin H C, Wang L L, Yang S Y, (1997). Extracting periodicity of a regular texture based on autocorrelation functions, Pattern Recognition Letters, 18:433-443.
• [11] Tae J K, Chang H K and Kyung W, (1999). Automatic Recognition of Fabric Weave Patterns by Digital Image Analysis, Textile Research Journal, 69(2):77-83.
• [12] Gordon J. Ross, (2014). Exponentially weighted moving average charts for detecting concept drift, 33(2):191-198.
• [13] Cleveland, W S, (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74(368): 829–836.
• [14] Raheja J L, Kumar S and Chaudhary A, (2013). Fabric defect detection based on GLCM and Gabor filter: A comparison, Optik - International Journal for Light and Electron Optics, 124(23):6469-6474.
• [15] Ravanidi S A H and Pan N, (2011). The influence of gray-level co-occurrence matrix variables on the textural features of wrinkled fabric surfaces. Journal of the Textile Institute, 102(4):315-321.
• [16] GAO S Z, (2008). Analysis of fabric texture based on GLCM, Computer Engineering and Design, 29(16):4385-4388.
• [17] CHANG L L, MA J, DENG Z M, (2008). Study on identification of fabric texture based on gray-level co-occurrence matrix, Chinese textile research, 29(10):43-46.
• [18] BENCO M and HUDEC R, (2007). Novel Method for Color Textures Features Extraction Based on GLCM, RADIOENGINEERING, 16(4) 64-67
• [19] Madhura C & Dheeraj D, (2013). Feature Extraction for Image Retrieval Using Color Spaces and GLCM International Journal of Innovative Technology and Exploring Engineering, 3(2):159-162.