Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (05): 72-78.doi: 10.13475/j.fzxb.20190806807

• Textile Engineering • Previous Articles     Next Articles

Detection for fabric defects based on low-rank decomposition

YANG Enjun, LIAO Yihui, LIU Andong(), YU Li   

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
  • Received:2019-08-27 Revised:2020-01-22 Online:2020-05-15 Published:2020-06-02
  • Contact: LIU Andong E-mail:lad@zjut.edu.cn

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Abstract:

Aiming at excessive loss of image information in fabric defect detection caused by the commonly used low-rank decomposition method and the weft skew caused by fabric elasticity, an improved low-rank decomposition detection method based on Beta norm was proposed. This method starts by constructing a prior map by extracting the texton feature of the fabric image. Second, a Beta norm was used to replace the nuclear norm in the low-rank decomposition process, whereas the low-rank decomposition was guided by the prior map to decompose the fabric image. Compared with the nuclear norm, it was found that the proposed method does not lead to excessive loss of image information. Furthermore, a posterior map was constructed by extracting the HOG (histogram of oriented gradients) feature of the fabric image, and a saliency map was obtained by the Hadamard product between the posterior map and the sparse component obtained by the low-rank decomposition which can solve the skew problem caused by fabric elasticity. Finally, optimal threshold segmentation was used to obtain the defect figure. Compared with the existing four methods, the experimental results demonstrate that the proposed method can effectively suppress the skewness in the fabric and the detection time is shorter.

Key words: fabric defect, defect detection, low-rank decomposition, posterior map, Beta norm, skew interference

CLC Number: 

  • TP391

Fig.1

Flow chart of proposed method"

Fig.2

Posterior map and saliency map. (a) Segmentation result of broken end; (b) Segmentation result of netting multiple; (c) Segmentation result of hole; (d) Segmentation result of oil"

Fig.3

Detection results for star-patterned fabric image. (a) Fabric image; (b) Method of reference [15]; (c) Method of reference [18]; (d) Method of reference [19]; (e) Method of reference [20]; (f) Method of this paper; (g) Ground-truth"

Fig.4

Detection results for box-patterned fabric image. (a) Fabric image; (b) Method of reference [15]; (c) Method of reference [18]; (d) Method of reference [19]; (e) Method of reference [20]; (f) Method of this paper; (g) Ground-truth"

Fig.5

Compared results of time for single fabric image"

Tab.1

Compared results of average time"

算法来源 平均用时/s
文献[15]
文献[19]
文献[20]
本文		 0.621
0.397
0.478
0.245

Fig.6

Compared results of total time for processing FID database"

Tab.2

Compared results of algorithms stability"

算法来源 标准差
文献[15]
文献[19]
文献[20]
本文		 0.445
0.044
0.036
0.032
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