Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (10): 58-66.doi: 10.13475/j.fzxb.20200102909

• Textile Engineering • Previous Articles     Next Articles

Fabric defect detection based on similarity location and superpixel segmentation

ZHU Lei(), REN Mengfan, PAN Yang, LI Botao   

  1. School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • Received:2020-01-20 Revised:2020-06-04 Online:2020-10-15 Published:2020-10-27

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Abstract:

Aiming at the problem in defect detection and accurate contour segmentation of periodic texture fabric image, a method of fabric defect detection was proposed based on similarity location and superpixel segmentation techniques. The median filter and logarithm enhancement were applied for the detected image, and the saliency image of the enhancement image was estimated by frequency-tuned algorithm to facilitate the preprocessing of the detected image. Combining gray similarity detection parameters based on the normalized local mean difference with structural similarity detection parameters, a similarity metric function capable of measuring more types of periodic texture fabric images was constructed. The rough localization of defects was identified by thresholding the similarity measurement value of the enhancement image blocks. Finally, superpixel fine segmentation and binarization were performed on the rough localization image blocks, and the outliers were eliminated via connected domain analysis to obtain a complete defect contour. The experimental results show that, compared with the three conrentional methods, the proposed method has a higher accuracy in detecting the defects in the periodic texture fabric image, and the extracted defect contour is more accurate.

Key words: fabric defect detection, similarity location, superpixel segmentation, similarity metric function, normalized local mean difference

CLC Number: 

  • TS101.9

Fig.1

Test images(a) and defect detection results by method of reference [7](b), reference [9](c) and reference [15](d)"

Fig.2

Flow chart of fabric defect detection method based on similarity location and superpixel segmentation"

Fig.3

Comparison on similarity rough location results of defects using SSIM and proposed method. (a) Test images; (b) Similarity rough location results of SSIM method; (c) Similarity rough location results of proposed method"

Fig.4

Similarity rough location results of defects at different weights α. (a) Test image; (b) α=0.7; (c) α=0.8; (d) α=0.9"

Fig.5

Comparison on segmentation results of similarity rough location image in Fig.3 using global threshold(a) and superpixel segmentation(b)"

Tab.1

Calculation process of proposed method"

方法1:基于相似性定位和超像素分割的织物疵点检测方法
输入:织物图像Ir
输出:检测结果B
1:使用式(1)将待检测图像Ir进行处理得到亮度信息Iv
2:对Iv进行中值滤波和对数增强,得到增强图像I
3: 利用式(4)估计增强图像I对应的显著图Ism
4:将I分为大小相同且互不重叠的N个小块Pn(n=1,2,…,N)和 以20为步长互相重叠的M个小块Qi(i=1,2,…,M)
5:for n=1:N
6: for i=1:M
7: if Pn≠Qi
8: S(Pn,Qi)=α×SSIM(Pn,Qi)+(1-α)×GRAY(Pn,Qi)
9: end if
10: end for
11: 使用式(11)对Pn进行处理,得到相似性粗定位图像Ic
12: end for
13:对Ic进行超像素分割,并使用式(13)对其进行二值化得到Bc
14:对Bc进行连通域分析,得到检测结果B

Fig.6

Comparison of defect detection results for simple texture fabric images using different methods. (a) Simple texture fabric images; (b) Defect detection results by method of reference [7]; (c) Defect detection results by method of reference [9]; (d) Defect detection results by method of reference [15]; (e) Defect detection results of proposed method"

Fig.7

Comparison on defect detection results for complex texture fabric images using different methods. (a) Complex texture fabric images; (b) Defect detection results by method of reference [7]; (c) Defect detection results by method of reference [9]; (d) Defect detection results by method of reference [15]; (e) Defect detection results of proposed method"

Tab.2

Comparison of parameters of fabric image detection results by different methods"

检测
方法		 输入织物
图像类型		 正确检
测数量		 错误检
测数量		 TPR FPR ACC T/s
文献[7] 含疵点 55 15 78.57 64.29 57.14 3.41
不含疵点 25 45
文献[9] 含疵点 54 16 77.14 71.43 52.86 0.16
不含疵点 20 50
文献[15] 含疵点 57 13 81.43 57.14 62.14 23.45
不含疵点 30 40
本文 含疵点 68 2 97.14 1.43 97.86 4.85
不含疵点 69 1
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