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

RichHTML

15

PDF (PC)

106

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
[1] 章友鹤, 赵连英. 环锭细纱机的技术进步与创新[J]. 纺织导报, 2015(1): 52-57.
ZHANG Youhe, ZHAO Lianying. Technological advancement and innovation of ring-spinning machine[J]. China Textile Leader, 2015(1): 52-57.
[2] 韩晨晨, 傅佳佳, 高卫东. 环锭细纱机的发展现状及技术进步[J]. 棉纺织技术, 2019, 47(2): 1-5.
HAN Chenchen, FU Jiajia, GAO Weidong. Development status and technology improvement of ring spinning frame[J]. Cotton Textile Technology, 2019, 47(2): 1-5.
[3] 唐新军, 宋均燕, 何小东. 国内外环锭纺自动接头装置的技术进展[J]. 棉纺织技术, 2019, 47(1): 78-84.
TANG Xinjun, SONG Junyan, HE Xiaodong. Technology progress of ring spinning automatic piecing device at home and abroad[J]. Cotton Textile Technology, 2019, 47(1): 78-84.
[4] 陈佳. 立达集团:自动化、紧密纺以及数字化领域创新突破[J]. 纺织导报, 2019 (7): 15.
CHEN Jia. Rieter group: innovative breakthroughs in automation, compact spinning and digitization[J]. China Textile Leader, 2019(7): 15.
[5] 唐火红, 周琼, 冯宝林, 等. 环锭纺接头机器人结构研究与动力学分析[J]. 机械设计与制造, 2016(1): 47-49,53.
TANG Huohong, ZHOU Qiong, FENG Baolin, et al. Structure research and dynamics analysis of yarn piecing robot for ring spinning[J]. Machinery Design & Manufacture, 2016(1): 47-49,53.
[6] XIAO R, XU Y, HOU Z, et al. An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding[J]. Sensors and Actuators A: Physical, 2019, 297:111533.
doi: 10.1016/j.sna.2019.111533
[7] YANG H, CHEN L, MA Z, et al. Computer vision-based high-quality tea automatic plucking robot using Delta parallel manipulator[J]. Computers and Electronics in Agriculture, 2021, 181:105946.
doi: 10.1016/j.compag.2020.105946
[8] 张文昌, 单忠德, 卢影. 基于机器视觉的纱笼纱杆快速定位方法[J]. 纺织学报, 2020, 41(12): 137-143.
ZHANG Wenchang, SHAN Zhongde, LU Ying. Fast location of yarn-bars on yarn-cage based on machine vision[J]. Journal of Textile Research, 2020, 41(12): 137-143.
[9] 张建新, 李琦. 基于机器视觉的筒子纱密度在线检测系统[J]. 纺织学报, 2020, 41(6): 141-146.
ZHANG Jianxin, LI Qi. Online cheese package yarn density detection system based on machine vision[J]. Journal of Textile Research, 2020, 41(6): 141-146.
[10] 王雯雯, 刘基宏. 应用优化霍夫变换的细纱断头检测[J]. 纺织学报, 2018, 39(4): 36-41.
WANG Wenwen, LIU Jihong. Spinning breakage detection based on optimized Hough transform[J]. Journal of Textile Research, 2018, 39(4): 36-41.
[11] 孙博文, 朱志明, 郭吉昌, 等. 基于组合激光结构光的视觉传感器检测算法及图像处理流程优化[J]. 清华大学学报(自然科学版), 2019, 59(6): 445-452.
SUN Bowen, ZHU Zhiming, GUO Jichang, et al. Detection algorithms and optimization of image processing for visual sensors using combined laser structured light[J]. Journal of Tsinghua Univer-sity(Science and Technology), 2019, 59(6): 445-452.
[12] 王文帝, 辛斌杰, 邓娜, 等. 单一视角下自适应阈值法的纱线毛羽识别及其应用[J]. 纺织学报, 2019, 40(5): 150-156.
WANG Wendi, XIN Binjie, DENG Na, et al. Identification and application of yarn hairiness using adaptive threshold method under single vision[J]. Journal of Textile Research, 2019, 40(5): 150-156.
[13] HAJJOUJI I, MARS S, ASRIH Z, et al. A novel FPGA implementation of Hough transform for straight lane detection[J]. Engineering Science and Technology, an International Journal, 2020, 23(2): 274-280.
doi: 10.1016/j.jestch.2019.05.008
[1] ZHANG Ronggen, FENG Pei, LIU Dashuang, ZHANG Junping, YANG Chongchang. Research on on-line detection system of broken filaments in industrial polyester filament [J]. Journal of Textile Research, 2022, 43(04): 153-159.
[2] XIONG Jingjing, YANG Xue, SU Jing, WANG Hongbo. Testing method for fabric moisture conductivity based on image technology [J]. Journal of Textile Research, 2021, 42(12): 70-75.
[3] LÜ Wentao, LIN Qiqi, ZHONG Jiaying, WANG Chengqun, XU Weiqiang. Research progress of image processing technology for fabric defect detection [J]. Journal of Textile Research, 2021, 42(11): 197-206.
[4] XIA Xuwen, MENG Shuo, PAN Ruru, GAO Weidong. On-line detection of warp collision and reed embedding based on improved inter-frame difference method [J]. Journal of Textile Research, 2021, 42(06): 91-96.
[5] JIANG Yanting, YAN Qingshuai, XIN Binjie, GAO Cong, SHI Meiwu. Comparative study on testing methods for unidirectional water transport in fabrics [J]. Journal of Textile Research, 2021, 42(05): 51-58.
[6] WU Liubo, LI Xinrong, DU Jinli. Research progress of motion trajectory planning of sewing robot based on contour extraction [J]. Journal of Textile Research, 2021, 42(04): 191-200.
[7] LI Dongjie, GUO Shuai, YANG Liu. Yarn defect detection based on improved image threshold segmentation algorithm [J]. Journal of Textile Research, 2021, 42(03): 82-88.
[8] TANG Qianhui, WANG Lei, GAO Weidong. Detection of fabric shape retention based on image processing [J]. Journal of Textile Research, 2021, 42(03): 89-94.
[9] TIAN Yuhang, WANG Shaozong, ZHANG Wenchang, ZHANG Qian. Rapid detection method of single-component dye liquor concentration based on machine vision [J]. Journal of Textile Research, 2021, 42(03): 115-121.
[10] FENG Wenqian, LI Xinrong, YANG Shuai. Research progress in machine vision algorithm for human contour detection [J]. Journal of Textile Research, 2021, 42(03): 190-196.
[11] MENG Shuo, XIA Xuwen, PAN Ruru, ZHOU Jian, WANG Lei, GAO Weidong. Detection of fabric density uniformity based on convolutional neural network [J]. Journal of Textile Research, 2021, 42(02): 101-106.
[12] ZHU Shigen, YANG Hongxian, BAI Yunfeng, DING Hao, ZHU Qiaolian. Investigation on automatic deformation inspection system of long and thin parts with hooks [J]. Journal of Textile Research, 2020, 41(10): 158-163.
[13] ZHANG Jianxin, LI Qi. Online cheese package yarn density detection system based on machine vision [J]. Journal of Textile Research, 2020, 41(06): 141-146.
[14] LU Hao, CHEN Yuan. Surface defect detection method of carbon fiber prepreg based on machine vision [J]. Journal of Textile Research, 2020, 41(04): 51-57.
[15] WANG Wensheng, LI Tianjian, RAN Yuchen, LU Ying, HUANG Min. Method for position detection of cheese yarn rod [J]. Journal of Textile Research, 2020, 41(03): 160-167.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd