Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (12): 50-56.doi: 10.13475/j.fzxb.20181200407

• Textile Engineering • Previous Articles     Next Articles

Fabric defect detection method based on improved fast weighted median filtering and K-means

ZHANG Huanhuan(), MA Jinxiu, JING Junfeng, LI Pengfei   

  1. School of Electronic and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • Received:2018-12-03 Revised:2019-06-02 Online:2019-12-15 Published:2019-12-18

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Abstract:

In order to detect various defects in the production process of textured fabrics, a texture fabric defect detection method based on improved weighted median filtering and K-means clustering was proposed. Firstly, the fabric image was preprocessed by the improved weighted median filter to reduce the influence of texture information on the defect detection. At the same time, by assigning weights and pixels to the histogram dynamic data, the time to seek the median was effectively shortened to increase the execution speed. Then, the K-means algorithm was adopted to cluster the filtered fabric images, and the cluster centers of the fabric image defects and non-defects were calculated, thereby realizing the segmentation of the image defect regions. The experimental results show that the method can effectively detect the defects of various types of textured fabrics such as square, dot, star, plain, and twill and significantly increase the detection speed.

Key words: fabric defect detection, improved weighted median filtering, joint histogram, K-means clustering

CLC Number: 

  • TP391

Fig.1

Flowchart of fabric defect detection"

Fig.2

Joint-histogram illustration"

Fig.3

Illustration of balance and cut point"

Fig.4

Segment of fabric defect by K-means. (a)Texture part; (b)Defect part; (c)Fabric defect image"

Fig.5

Detection results of different r. (a)Grid beheaded; (b) r=10.0; (c) r=13.0; (d) r=15.0"

Fig.6

Detection results of different σ. (a) Box-patterned oil; (b) σ=100.0; (c) σ=135.0; (d) σ=500.0"

Tab.1

Parameters setting for modified faster weighted median filter"

织物类型 r σ
方格 [12.5~13.0] [135.0~245.0]
点形 [8.0~11.0] [90.0~200.0]
星形 [55.0~58.0] [250.0~390.0]
平纹 [5.0~10.0] [12.0~25.0]
斜纹 [9.0~10.0] [21.5~25.0]

Fig.7

Some results of square, dot, star fabric defect detection by method of this paper. (a) Fabric original image; (b) Result by using improved weighted median filtering; (c) Detection result"

Fig.8

Some results of plain and twill fabric defect detection by method of this paper. (a) Fabric original image; (b) Result by using improved weighted median filtering; (c) Detection result"

Fig.9

Comparison of results by different method. (a) Fabric original image; (b) Detection result by method of reference[3]; (c) Detection result by method of reference[17]; (d) Detection result by method of this paper"

Tab.2

Comparison of average time by three different methodss"

方法 方格
织物		 点形
织物		 星形
织物		 平纹
织物		 斜纹
织物
文献[3] 9.156 7 14.998 6 8.536 5 10.015 8 38.782 4
文献[17] 0.941 5 0.913 4 1.095 7 0.974 1 49.459 7
本文方法 0.193 7 0.233 4 0.312 2 0.049 5 0.507 7

Tab.3

Comparison of accuracy by three different methods"

织物类型 检测方法 ACC/% TPR/% FPR/%
文献[3] 93.93 95.07 12.87
方格 文献[17] 92.50 94.29 20.00
本文方法 97.50 98.09 6.67
文献[3] 92.49 96.01 29.97
点形 文献[17] 95.09 97.82 20.00
本文方法 94.17 95.24 13.57
文献[3] 93.62 97.14 25.88
星形 文献[17] 91.67 97.73 13.29
本文方法 94.36 95.89 19.73
文献[3] 95.83 98.09 20.05
平纹 文献[17] 90.94 95.23 30.48
本文方法 94.23 96.84 25.75
文献[3] 91.58 98.56 10.98
斜纹 文献[17] 92.79 96.76 17.79
本文方法 94.64 93.42 5.96
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