Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (09): 59-66.doi: 10.13475/j.fzxb.20191204308

• Textile Engineering • Previous Articles     Next Articles

Fabric defect detection method based on primitive segmentation and Gabor filtering

DI Lan1,2(), YANG Da1, LIANG Jiuzhen3, MA Mingyin1   

  1. 1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
    2. Key Laboratory of Ministry of Public Security for Road Traffic Safety, Wuxi, Jiangsu 214151, China
    3. College of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
  • Received:2019-12-17 Revised:2020-06-04 Online:2020-09-15 Published:2020-09-25

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Abstract:

In order to deal with the complex fabric defect detection with periodic variation pattern, a fabric defect detection method based on primitive segmentation and Gabor filtering was proposed. The template size of the pattern unit was determined according to the periodic variation of the image texture, i.e. a lattice containing a periodic pattern. The image was adaptively segmented, and the image elements of the smaller unit are obtained by the primitive segmentation. The response distribution given by the Gabor filter to the convolutional lattice produced an ideal template lattice. According to the proposed voting procedure, the lattice of the defect is identified by analyzing the distribution of the lattice differences represented by the Manhattan distance of the eigenvectors. Experiments show that the method has better detection effect on the fabric samples of star and box patterns, and significantly reducing the recall rate of the samples.

Key words: primitive segmentation, Gabor filtering, fabric defect, defect inspection

CLC Number: 

  • TS131.9

Fig.1

Get image unit template size. (a)Sample image; (b)Split image"

Fig.2

Segmentation of star images. (a)Upper left primitive;(b)Upper right primitive; (c)Lower left primitive;(d)Lower right primitive"

Fig.3

Gabor filter convolution project"

Fig.4

Characteristic vector after different latiice convolution"

Tab.1

Different algorithms for detection of star-shaped"

星状图 MTPR/
%		 MFPR/
%		 MPPV/
%		 MNPV/
%		 f值/
%		 方法
86 2 20 99 33 本文算法
31 16 1 99 5 BB
断端 32 2 11 100 5 RB
73 4 9 100 5 WGIS
1 0 0 99 15 ER
81 1 26 99 40 本文算法
33 15 1 100 4 BB
破洞 43 3 8 100 5 RB
26 7 3 100 2 WGIS
4 0 0 99 0 ER
82 0 15 99 2 本文算法
6 0 22 98 10 BB
细条纹 3 0 27 98 6 RB
0 3 0 99 0 WGIS
45 2 12 99 2 ER
75 1 71 99 73 本文算法
10 16 2 96 56 BB
粗条纹 21 3 13 97 54 RB
89 19 20 100 8 WGIS
38 1 57 99 29 ER

Fig.5

MFPR-MTPR figures of star-shaped by 5 detection methods"

Fig.6

Fabric detection results using different algorithms. (a) Original image; (b) Ground-truth;(c)Algorithm of this paper; (d) BB; (e) RB; (f) WGIS; (g) ER"

Tab.2

Different algorithms for detection of box-shaped"

盒状图 MTPR/
%		 MFPR/
%		 MPPV/
%		 MNPV/
%		 f值/
%		 方法
79 2 38 99 51 本文算法
4 2 4 98 3 BB
断端 49 1 56 99 6 RB
64 8 14 99 0 WGIS
1 0 0 98 8 ER
68 1 30 99 57 本文算法
8 2 3 99 4 BB
破洞 10 0 47 99 11 RB
2 1 4 99 0 WGIS
0 0 0 99 16 ER
73 4 15 99 24 本文算法
31 0 29 99 31 BB
细条纹 32 0 30 99 31 RB
26 24 1 99 2 WGIS
5 4 2 97 3 ER
81 3 33 97 47 本文算法
8 2 8 97 41 BB
粗条纹 58 1 68 99 31 RB
99 14 15 100 5 WGIS
2 0 0 98 61 ER

Fig.7

MFPR-MTPR figures of box-shaped by 5 detection methods"

[1] BU H G, WANG J, HUANG X B. Fabric defect detection based on multiple fractal features and support vector data description[J]. Engineering Applications of Artificial Intelligence, 2009,22(2):224-235.
doi: 10.1016/j.engappai.2008.05.006
[2] 田承泰, 步红刚, 汪军, 等. 基于时间序列分形特征的织物瑕疵检测[J]. 纺织学报, 2010(5):49-52,59.
TIAN Chengtai, BU Honggang, WANG Jun, et al. Fabric defect detection based on frature of timie series[J]. Journal of Textile Research, 2010(5):49-52,59.
[3] 张波, 汤春明. 基于相对总变差模型与自适应形态学的织物瑕疵检测[J]. 纺织学报, 2017,38(5):145-149.
ZHANG Bo, TANG Chunming. Fabric defect detection based on relative total variation model and adaptive mathematical morphology[J]. Journal of Textile Research, 2017,38(5):145-149.
[4] ZHANG C S, KE W, WANG G H. Automatic recognition analysis of fabric structure based on GLCM and BP neural network[J]. Advanced Materials Research, 2011, 332-334:1167-1170.
[5] ZHU D, PAN R, GAO W, et al. Yarn-dyed fabric defect detection based on autocorrelation function and GLCM[J]. Autex Research Journal, 2015,15(3):226-232.
[6] MAK K L, PENG P, YIU K F C. Fabric defect detection using morphological filters[J]. Image & Vision Computing, 2009,27(10):1585-1592.
[7] CHETVERIKOV D, HANBURY A. Finding defects in texture using regularity and local orientation[J]. Pattern Recognition, 2002,35(10):2165-2180.
[8] YANG X Z, PANG G K H, YUNG N H C. Discriminative fabric defect detection using adaptive wavelets[J]. Optical Engineering, 2002,41(12):3116-3126.
[9] ZUO H, WANG Y, YANG X, et al. Fabric defect detection based on texture enhancement[C]// 2012 5th International Congress on Image and Signal Processing (CISP). Philadelphia: IEEE, 2012: 876-880.
[10] NAVARRO P J, FERNÁNDEZISLA C, ALCOVER P M, et al. Defect detection in textures through the use of entropy as a means for automatically selecting the wavelet decomposition level[J]. Sensors, 2016,16(8):21.
[11] ZHU Dandan, PAN Ruru, GAO Weidong. Fabric defect detection using characteristic spectrum of Fourier transform and correlation coefficient[J]. Computer Engineering and Applications, 2014,50(10):866-73.
[12] 屈博, 卢朝阳, 李静, 等. 一种改进的多通道Gabor滤波器布匹瑕疵检测方法[J]. 纺织学报, 2009,30(12):37-40.
QU Bo, LU Zhaoyang, LI Jing, et al. An improved multichannel Gabor filter algorithm for fabric defect detection[J]. Journal of Textile Research, 2009,30(12):37-40.
[13] MAK K L, PENG P, YIU K F C. Fabric defect detection using multi-level tuned-matched Gabor filters[J]. Journal of Industrial and Management Optimization, 2012,8(2):325-341.
doi: 10.3934/jimo.2012.8.325
[14] MAK K L, PENG P. An automated inspection system for textile fabrics based on Gabor filters[J]. Robotics and Computer Integrated Manufacturing, 2008,24(3):359-369.
[15] BU H G, HUANG X B, WANG J, et al. Detection of fabric defects by auto-regressive spectral analysis and support vector data description[J]. Textile Research Journal, 2010,80(7):579-589.
[16] SERAFIM A F L. Multiresolution pyramids for segmentation of natural images based on autoregressive models: application to calf leather classification[C]// Proceedings of Conference of the IEEE Industrial Electronics Society. Philadelphia: IEEE, 2002: 1842-1847.
[17] HAJIMOWLANA S H, MUSCEDERE R, JULLIEN G A, et al. 1D autoregressive modeling for defect detection in web inspection systems[C]// Midwest Symposium on Systems & Circuits. Washington: IEEE Computer Society, 1998: 318-321.
[18] DENG H, CLAUSI D A. Gaussian MRF rotation-invariant features for image classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004,26(7):951-955.
pmid: 18579954
[19] COHEN F S, FAN Z, ATTALI S, et al. Automated inspection of textile fabrics using textural models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991,13(8):803-808.
[20] 纪旋, 梁久祯, 侯振杰, 等. 基于模板校正与低秩分解的纺织品瑕疵检测方法[J]. 模式识别与人工智能, 2019,32(3):78-87.
JI Xuan, LIANG Jiuzhen, HOU Zhenjie, et al. Fabric defect detection based on template correction and low-rank decomposiont[J]. Pattern Recognition and Artificial Intelligence, 2019,32(3):78-87.
[21] NGAN H Y T, PANG G K H, YUNG S P, et al. Wavelet based methods on patterned fabric defect detection[J]. Pattern Recognition, 2005,38(4):559-576.
doi: 10.1016/j.patcog.2004.07.009
[22] NGAN H Y T, PANG G K H. Novel method for patterned fabric inspection using Bollinger bands[J]. Optical Engineering, 2006,45(8):087202.
[23] NGAN H, PANG G. Regularity analysis for patterned texture inspection[J]. IEEE Transactions on Automation Science and Engineering, 2009,6(1):131-144.
[24] NG M K, NGAN H Y T, YUAN X, et al. Patterned fabric inspection and visualization by the method of image decomposition[J]. IEEE Transactions on Automation Science & Engineering, 2014,11(3):943-947.
[25] TSANG C S C, NGAN H Y T, PANG G K H. Fabric inspection based on the Elo rating method[J]. Pattern Recognition, 2016,51:378-394.
[26] 刘威, 常兴治, 梁久祯, 等. 基于局部最优分析的纺织品瑕疵检测方法[J]. 模式识别与人工智能, 2018(2):182-189.
LIU Wei, CHANG Xingzhi, LIANG Jiuzhen, et al. Textile defect detection method based on local optimal analysis[J]. Pattern Recognition and Artificial Intelligence, 2018 (2):182-189.
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