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"

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