Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (05): 150-156.doi: 10.13475/j.fzxb.20180404607

• Management & Information • Previous Articles     Next Articles

Identification and application of yarn hairiness using adaptive threshold method under single vision

WANG Wendi, XIN Binjie(), DENG Na, LI Jiaping, LIU Ningjuan   

  1. Fashion College, Shanghai University of Engineering Science, Shanghai 201620, China
  • Received:2018-04-19 Revised:2019-02-12 Online:2019-05-15 Published:2019-05-21
  • Contact: XIN Binjie E-mail:xinbj@sues.edu.cn

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Abstract:

In order to measure the yarn parameter information more accurately, the image grayscale enhancement algorithm and the linear region threshold segmentation algorithm were proposed to solve the serious loss of hairiness information after yarn image processing with the image background processing and the threshold segmentation algorithm. Using the self-built image acquisition system, six different types of yarn samples were acquired, and then the accuracy and validity of the image recognition algorithm was verified. Experimental results show that the proposed two algorithms can significantly reduce the loss of yarn image information and have good robustness. The length and number of yarn hairiness detected by the image processing method are similar to those of the visual inspection method. The grayscale contrast of the yarn and yarn image background is enhanced, and the effect of poor image segmentation due to a single threshold is avoided, thereby improving the recognition accuracy and measurement accuracy of the yarn hairiness. The research results provide an effective yarn image analysis algorithm for the subsequent development of a commercial yarn hairiness detection system.

Key words: yarn hairiness, adaptive grayscale enhancement, regional threshold segmentation, image processing, image analysis

CLC Number: 

  • TP319

Fig.1

Yarn hairiness image acquisition system. (a) Schematic diagram of yarn image acquisition system; (b) Dark box structures"

Tab.1

Basic parameters of yarn sample"

试样编号 纱线成分 成纱方式 梳纱工艺 纱线线密度/tex
1# 棉 环锭纺 精梳 14
2# 棉 环锭纺 精梳 18
3# 棉 环锭纺 精梳 26
4# 棉 环锭纺 普梳 26
5# 棉 转杯纺 精梳 28
6# 涤纶 环锭纺 精梳 28

Fig.2

Original yarn images"

Fig.3

Image difference processing. (a) Background image;(b) Partial enlargement of image after image differential processing; (c) Gray value histogram after image difference processing; (d) Local gray value matrix of image after image difference processing"

Fig.4

Image grayscale contrast after background removal. (a) Background removal image IA;(b) Gray stretched enhanced image IB;(c) Adaptive grayscale enhancement image IC"

Fig.5

Image local gray value matrix after image processing. (a) Local gray value matrix of image IA;(b) Local gray value matrix of image IB; (c) Local gray value matrix of image IC"

Fig.6

Wiener filtered image comparison. (a) Image IB after treatment (ID); (b) Image IC after treatment (IE)"

Fig.7

Image comparison after OTSU threshold segmentation. (a) Image ID after processing (IF);(b) Image IE after processing (IG)"

Fig.8

Image comparison after OTSU threshold segmentation. (a) Image ID after grayscale processing and wiener filter treatment for two times (IH);(b) Image (IE) after grayscale processing and wiener filter treatment for three times (IK)"

Fig.9

Comparison between linear region threshold segmentation and full threshold segmentation. (a) Threshold segmented image of IL;(b) Linear region threshold segmentation image of IM"

Fig.10

Yarn image morphology processing. (a) Image IM after morphological processing; (b) Image IO after morphological processing;(c) Image IP after thinning image IO"

Tab.2

Comparison of image method and visual inspection for detecting length and number of yarn hairiness"

编号 毛羽长度/
mm		 毛羽根数 偏差/%
图像法 目测法
1 107.4 86.7 23.88
2 23.3 22.3 4.5
1# 3 5.1 5.1 0
4 1.2 1.2 0
5 0.6 0.6 0
6 0 0 0
1 127.3 87.6 45.32
2 13.6 13.2 3.03
2# 3 1.3 1.3 0
4 0 0 0
5 0 0 0
6 0 0 0
1 75.5 67.3 12.18
2 10.1 8.7 16.09
3# 3 0.9 0.9 0
4 0.2 0.2 0
5 0.1 0.1 0
6 0 0 0
1 107.8 92.3 16.79
2 20.1 18.8 6.91
4# 3 2.8 2.8 0
4 0.3 0.3 0
5 0.1 0.1 0
6 0 0 0
1 117.3 86.7 35.29
2 23.1 19.6 17.86
5# 3 2.6 2.6 0
4 0.2 0.2 0
5 0 0 0
6 0 0 0
1 76.3 63.8 19.59
2 12.8 11.3 13.27
6# 3 5.2 5.1 1.96
4 0.3 0.3 0
5 0 0 0
6 0 0 0
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