Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (03): 82-88.doi: 10.13475/j.fzxb.20200700407

• Textile Engineering • Previous Articles     Next Articles

Yarn defect detection based on improved image threshold segmentation algorithm

LI Dongjie1,2(), GUO Shuai1, YANG Liu1   

  1. 1. College of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China
    2. Key Laboratory of Intelligent Technology for Cutting and Manufacturing, Ministry of Education,Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China
  • Received:2020-07-01 Revised:2020-10-21 Online:2021-03-15 Published:2021-03-17

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Abstract:

Aiming at the problems of poor reliability, low sensitivity and low speed of yarn defect detection in the textile industry, a new yarn defect detection method based on digital image processing was proposed. A yarn image acquisition system is built to obtain yarn image. In view of the difficulty of yarn edge information processing and the poor effect of traditional bilateral filtering on pepper-and-salt noise processing, the bilateral filtering was worked on for improvement, and the improved bilateral filtering was shown to be effective for preserving the yarn edge data. Furthermore, aiming at the problem of large amount of calculation and difficulty in finding the optimal threshold, the optimal threshold calculation method of traditional threshold segmentation algorithm is improved. The improved threshold segmentation algorithm not only ensures the processing effect, but also improves the processing speed of the whole algorithm. Sub-pixel is used to calculate the yarn edge and improve the accuracy of yarn defect detection. The experimental results verified the effectiveness and reliability of the algorithm, and increased the detection speed by more than 20% while improving the accuracy, which is of great significance for improving the accuracy of yarn quality detection.

Key words: defect detecting, yarn defect, threshold segmentation, sub-pixel point judgment, image processing

CLC Number: 

  • TH74

Tab.1

Internal parameters of CCD camera"

参数 数值 参数 数值
ax/mm 520.547 9 k1 -0.803 4
ay/mm 805.510 6 k2 2.613 0
u0/mm 370 k3 0.000 0
v0/mm 325 p1 -0.005 3
μ 0.00 p2 -0.001 5

Fig.1

Collected yarn image. (a) Coarse yarn; (b) Fine yarn; (c) Normal yarn"

Fig.2

Gray image processing of yarn. (a) Coarse yarn; (b) Fine yarn; (c) Normal yarn"

Fig.3

Range kernel approximation curve"

Fig.4

Yarn image after filtering. (a) Coarse yarn; (b) Fine yarn; (c) Normal yarn"

Fig.5

Yarn binary image processing diagram. (a) Coarse yarn; (b) Fine yarn; (c) Normal yarn"

Fig.6

Yarn mathematical morphology processing diagram. (a)Expansion diagram of coarse yarn; (b) Corrosion diagram of coarse yarn; (c)Expansion diagram of fine yarn;(d) Corrosion diagram of fine yarn; (e)Expansion diagram of normal yarn; (f) Corrosion diagram of normal yarn"

Fig.7

Subpixel edge detection. (a) Coarse yarn; (b) Fine yarn; (c) Normal yarn"

Fig.8

Yarn detection results. (a) 14.6 tex yarn;(b) 18.2 tex yarn;(c) 27.8 tex yarn"

Fig.9

Comparison of yarn diameter"

Tab.2

Sample test results"

疵点
类型		 传统方法 亚像素方法
平均直
径/mm		 直径偏差
率/%		 平均处理
时间/s		 平均直
径/mm		 直径偏差
率/%		 平均处理
时间/s
正常 0.149 6 2.46 0.125 0.147 9 1.30 0.107
粗节 0.223 1 52.81 0.132 0.221 8 51.91 0.105
细节 0.102 5 29.79 0.122 0.104 6 28.35 0.102

Fig.10

Yarn control interface. (a)Yarn qualification drawing interface; (b)Yarn unqualification drawing interface"

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