Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (11): 64-70.doi: 10.13475/j.fzxb.20201200807

• Textile Engineering • Previous Articles     Next Articles

Fabric defects segmentation using total variation

LIU Guowei, PAN Ruru, GAO Weidong, ZHOU Jian()   

  1. Key Laboratory of Eco-Textiles( Jiangnan University), Ministry of Education, Wuxi, Jiangsu 214122, China
  • Received:2020-12-03 Revised:2021-08-03 Online:2021-11-15 Published:2021-11-29
  • Contact: ZHOU Jian E-mail:jzhou@jiangnan.edu.cn

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Abstract:

Aiming at the problem of insufficient accuracy of the current woven fabric defect detection methods, this paper proposes a fabric defect segmentation method based on the total variation model, focusing on solving unobvious defects along warp and weft directions. The singular value decomposition low-rank was used to reconstruct the textile texture image by removing the texture background of the fabric so as to obtain the defect abnormal map. Following that, by constructing a total variation model, the defect anomaly map was optimized to obtain the enhanced abnormal map under different constraints. Finally, the accurate segmentation of defects was achieved through adopting the conventional segmentation algorithms. The experimental results show that the separability of the defects and the background of the defect abnormal image processed by the proposed total variation model has been significantly improved. The influence of the parameters of the model on the segmentation results was discussed to further verify the effectiveness and stability of the method.

Key words: fabric defect, texture low-rank reconstruction, total variation model, defect segmentation, image recognition

CLC Number: 

  • TS101.91

Fig.1

SVD reconstruction renderings. (a) Original image; (b) Reconstructed image; (c) Defect abnormal image"

Fig.2

Enhancement image in weft (a)and warp(b) direction and enhancement image of total variation model(c)"

Fig.3

Enhancement effect images of defect. (a) Original image; (b) Defect abnormal image; (c) Enhancement image; (d) Enhancement image in weft direction; (e) Enhancement image in warp direction"

Fig.4

Segmentation result images. (a) Segmentation result of Fig.1(a); (b) Segmentation result of Fig.3(a)"

Tab.1

Parameters of 8 sample fabrics"

样本
编号		 密度/(根·(10 cm)-1) 疵点 组织
经向 纬向
1# 402 254 沉纱 斜纹
2# 348 276 双纬 平纹
3# 292 224 缺纬 斜纹
4# 346 274 双纬 斜纹
5# 428 296 纬缩 平纹
6# 428 296 稀纬 平纹
7# 538 340 紧经 平纹
8# 438 304 松经 斜纹

Fig.5

Several manually marked defect areas. (a) Original images and manually marked defect areas of sample 1#; (b) Original images and manually marked defect areas of sample 3#; (c) Original images and manually marked defect areas of sample 5#"

Fig.6

F curve under different λ"

Fig.7

Smooth term curve of fabric sample 7#"

Fig.8

Smooth term difference curve of fabric samples"

Tab.2

Test results of each test sample"

样本编号 λ值 F值
1# 0.106 0.96
2# 0.064 0.98
3# 0.064 1.00
4# 0.106 0.93
5# 0.064 0.84
6# 0.085 0.92
7# 0.085 0.81
8# 0.085 0.74

Fig.9

Visualized segmentation results. (a) Original images; (b) Defect abnormal images; (c) Defect enhancement images; (d) Segmentation results of Fig.(b) using threshold method; (e) Segmentation results of Fig.(c) using threshold method"

[1] ZHOU Jian, WANG Jun. Unsupervised fabric defect segmentation using local patch approximation[J]. Journal of The Textile Institute, 2016, 107(6):1-10.
doi: 10.1080/00405000.2014.999478
[2] LATIF-AMET A, ERTÜZÜN A, ERÇIL A. An efficient method for texture defect detection: sub-band domain co-occurrence matrices[J]. Image and Vision Computing, 2000, 18(6):543-553.
doi: 10.1016/S0262-8856(99)00062-1
[3] CHAN C H, PANG G K H. Fabric defect detection by Fourier analysis[J]. IEEE Trans on Ind Appl, 2000, 36(5):1267-1276.
doi: 10.1109/28.871274
[4] 李仁忠, 张缓缓, 景军锋, 等. 基于EM算法的高斯混合型的织物疵点检测研究[J]. 计算机工程与应用, 2014, 50(10):184-187.
LI Renzhong, ZHANG Huanhuan, JING Junfeng, et al. Fabric defect detection based on Gaussian mixture models of EM algorithm[J]. Computer Engineering and Applications, 2014, 50(10):184-187.
[5] 潘如如, 高卫东, 钱欣欣, 等. 基于互相关的印花织物疵点检测[J]. 纺织学报, 2010, 31(12):134-138.
PAN Ruru, GAO Weidong, QIAN Xinxin, et al. Defect detection of printed fabrics using normalized cross correlation[J]. Journal of Textile Research, 2010, 31(12):134-138.
[6] JING Junfeng, MA Hao, ZHANG Huanhuan. Automatic fabric defect detection using a deep convolutional neural network[J]. Coloration Technology, 2019, 135(3):2-4.
[7] 厉征鑫, 周建, 潘如如, 等. 应用单演小波分析的织物疵点检测[J]. 纺织学报, 2016, 37(9):59-64.
LI Zhengxin, ZHOU Jian, PAN Ruru, et al. Fabric defect detection using monogenic wavelet analysis[J]. Journal of Textile Research, 2016, 37(9):59-64.
[8] 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 Chaoyang, LI Jing, et al. An improved multichannel Gabor filter algorithm for fabric defect detection[J]. Journal of Textile Research, 2009, 30(12):37-40.
doi: 10.1177/004051756003000104
[9] 祝双武, 郝重阳. 基于纹理周期性分析的织物疵点检测方法[J]. 计算机工程与应用, 2012, 48(21):163-166.
ZHU Shuangwu, HAO Chongyang. Fabric defect detection approach based on texture periodicity analysis[J]. Computer Engineering and Applications, 2012, 48(21):163-166.
[10] ANTONIN Chambolle. An algorithm for total variation minimization and applications[J]. Journal of Mathematical Imaging and Vision, 2004, 20(1/2):89-97.
doi: 10.1023/B:JMIV.0000011321.19549.88
[11] BRESSON X, CHAN T F. Fast dual minimization of the vectorial total variation norm and applications to color image processing[J]. Inverse Problems and Imaging, 2008(2):455-484.
[12] CHATTERJEE. An introduction to the proper orthogonal decomposition[J]. Current Science, 2000, 78:808-817.
[13] ZHOU Jian, WANG Jun. A real-time computer vision-based platform for fabric inspection: part 1: algorithm[J]. Journal of The Textile Institute, 2015, 106(12):1282-1292.
doi: 10.1080/00405000.2014.996333
[14] 刘红毅. 结构保持的图像去噪方法研究[D]. 南京:南京理工大学, 2011:10-13.
LIU Hongyi. Structure preserved image denoising algorithms[D]. Nanjing: Nanjing University of Science and Technology, 2011:10-13.
[15] 李燕民, 季维勋, 李维国. 一种新的图像去噪方法[J]. 微计算机应用, 2010, 31(3):17-21.
LI Yanmin, JI Weixun, LI Weiguo. A new method for image denoising[J]. Microcomputer Applications, 2010, 31(3):17-21.
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