Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (08): 67-73.doi: 10.13475/j.fzxb.20210505007

• Textile Engineering • Previous Articles     Next Articles

Density detection method of weft knitted fabrics making use of combined image frequency domain and spatial domain

DENG Zhongmin, HU Haodong, YU Dongyang, WANG Wen, KE Wei()   

  1. State Key Laboratory of New Textile Materials and Advanced Processing Technology, Wuhan Textile University, Wuhan, Hubei 430200, China
  • Received:2021-05-20 Revised:2022-04-15 Online:2022-08-15 Published:2022-08-24
  • Contact: KE Wei E-mail:wke@wtu.edu.cn

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Abstract:

Aiming at the problems of low accuracy and poor applicability in measuring the density of weft knitted fabrics based on image processing, this paper presents a method for locating the loop position based on the gray curve of weft knitted fabric image to measure the fabric density, which highlights the loop image based on discrete wavelet transform. By extracting the gray curves of weft knitted fabrics and binary image respectively, the coordinates of the actual loop column were obtained by using the trough coordinate verification algorithm based on probability density statistics and the loop coordinate verification algorithm proposed in this study. The eight-neighborhood search algorithm was used to calculate the number of loops in the transverse and longitudinal directions of the weft knitted fabric, and the course and wale densities of the weft knitted fabric were obtained. Compared with the existing methods based on image processing, the method proposed in this paper involves less computation, and the error is less than 1.7％ compared with the standard data, which shows that this method has fast measurement and high accuracy, and it is beneficial for automatic measurement of weft knitted fabric density.

Key words: density of knitted fabric, image processing, gray curve, connected domain, density detection method

CLC Number: 

  • TS107

Fig.1

Schematic diagram of fabric structure"

Fig.2

Overall flow chart"

Fig.3

Image tilt correction. (a) Image gray curve correction without Hough correction; (b)Image gray curve after Hough correction"

Fig.4

Discrete wavelet decomposition diagram. (a)Original image; (b)N=1; (c)N=2; (d)N=3; (e)N=4; (f)N=5; (g)N=6; (h)N=7; (g)Reconstructed image"

Fig.5

Gray scale curve of image. (a)Coordinate map of though obtained from original gray curve; (b) Trough coordinate map obtained after removing impurities from original gray curve"

Fig.6

Contrast diagram of binarization treatment. (a)OTSU processing image; (b)Local adaptive threshold binarization image"

Fig.7

Simulation diagram of disconnection between yarn interval area and loop area"

Fig.8

Images before(a) and after(b) eliminating clearance column"

Fig.9

Images before(a) and after(b) morphological operation"

Fig.10

Gray scale curve of binary image. (a)Coordinate map of trough obtained from original gray curve; (b)Trough coordinate map obtained after removing impurities from original gray curve"

Tab.1

Part of experimental measurement results and comparison"

试样
编号		 本文方法 横密误
差/％		 纵密误
差/％		 人工检测 方法1 方法2
横密 纵密 时间/s 横密 纵密 时间/s 横密 纵密 时间/s 横密 纵密 时间/s
1 90.5 137.1 0.44 0.56 0.07 90.0 137.0 — 90.0 136.8 0.46 181.1 138.1 1.30
2 61.0 74.6 0.37 1.67 0.50 60.0 75.0 — 60.0 74.8 0.51 60.7 75.0 0.46
3 59.8 84.0 0.23 0.33 1.18 60.0 85.0 — 59.5 85.2 0.41 60.0 84.4 0.32
4 29.8 60.9 0.21 0.67 1.50 30.0 60.0 — 30.0 59.2 0.43 28.2 59.4 0.34
5 49.7 75.3 0.81 0.60 0.40 50.0 75.0 — 20.0 565.8 3.24 48.2 74.8 0.37
6 75.5 108.9 0.65 0.67 1.00 75.0 110.0 — 74.1 109.2 0.68 74.7 112.7 0.61

Fig.11

Processing flow chart of method 1. (a)Corrected process flow; (b)Abnormal processing flow"

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