Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (05): 163-169.doi: 10.13475/j.fzxb.20210504407

• Machinery & Accessories • Previous Articles     Next Articles

Yarn breakage location for yarn joining robot based on machine vision

ZHOU Qihong1,2(), PENG Yi1,2, CEN Junhao3, ZHOU Shenhua1, LI Shujia1   

  1. 1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
    2. Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620, China
    3. Guangzhou Seyounth Automation Technology Co., Ltd., Guangzhou, Guangdong 511400, China
  • Received:2021-05-18 Revised:2021-11-20 Online:2022-05-15 Published:2022-05-30

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Abstract:

In order to identify and locate the yarn breakage in the spinning process for the yarn joining robot through visual method and to simplify the mechanical structure, a recognition and positioning algorithm for yarn characteristics is proposed according to the image characteristics. An industrial camera was used to collect the image of the yarn being sucked into the suction nozzle, and the contrast between yarn features and background was enhanced through an improved gray enhancement method, using Canny operator for yarn edge detection. The image features of the yarn were obtained by dividing the interest regions and optimized using Hough line detection method, and the positioning algorithm was used to extract the required location information. The experimental results show that the position information extracted by the proposed algorithm has high accuracy, the error of coordinate points is 1.42 pixels, and the error of angle α is 0.60°. Compared with the use of the traditional location detecting algorithm, the running time of the program is reduced, and the average recognition time is in the order of 10-1 s, with good real-time performance. The research results can be applied to the development of yarn joining robot products.

Key words: yarn joining, machine vision, yarn breakage location, image processing, Hough transform

CLC Number: 

  • TS103.2

Fig.1

Schematic diagram of device"

Fig.2

Positioning principle"

Fig.3

Yarn state. (a) No fluctuation of yarn; (b) Yarn fluctuates"

Fig.4

Improve the grayscale enhancement process diagram. (a) Original image I; (b) Background image b; (c) Improved grayscale enhancement image O"

Fig.5

Local gray value distribution diagram. (a) Local gray value distribution diagram of image I; (b) Local gray value distribution diagram of image O"

Fig.6

Comparison diagram of threshold segmentation and Canny detection. (a) Original image; (b) Binary segmentation; (c) Canny detection"

Fig.7

Hough transform schematic"

Fig.8

Upper and lower ROI area Angle limit position"

Tab.1

ROI section r and θ parameter table"

图像 上ROI区域 下ROI区域
r θ/(°) r θ/(°)
图像1 210.6 50 579 100
图像2 234.6 58 540.6 106
图像3 251.4 60 550.2 112
图像4 282.6 66 395.4 128
图像5 310.2 74 309 135.9
图像6 333 86 303.3 137.9
图像7 448 92 279 139.9

Fig.9

Partial yarn image coordinate extraction. (a) Image one; (b) Image two; (c) Image three; (d) Image four; (e) Image five; (f) Image six"

Tab.2

Image location results"

图像 程序输出坐标/像素 手动标注坐标/像素 V轴绝对误差/像素 程序输出α/(°) 手动标注α/(°) α值绝对误差/(°)
图像1 (140,606) (140,609.5) 3.5 15.01 15.82 0.81
图像2 (140,393) (140,392) 1 174.99 175.24 0.25
图像3 (140,530) (140,531.5) 1.5 5.01 5.24 0.23
图像4 (140,479) (140,478.5) 0.5 1.99 2.86 0.87
图像5 (140,459) (140,460) 1 178.99 177.61 1.38
图像6 (140,139) (140,140) 1 164.99 165.07 0.08
平均误差 — — 1.42 — — 0.60

Tab.3

Total algorithm time"

图像 本文算法 OHT 直线拟合
图像1 53.9 21.9 433.7
图像2 59.1 17.9 460.8
图像3 63.9 24 513.7
图像4 61.9 16 511.7
图像5 79.9 23.9 453.7
图像6 60 34 497.7
平均耗时 63.1 23 478.6
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