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"

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