Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (8): 164-168.doi: 10.13475/j.fzxb.20180602505

• Management & Information • Previous Articles     Next Articles

Detection method of cohesive performance of raw silk based on machine vision

SUN Weihong(), RUAN Mianjiang, SHAO Tiefeng, LIANG Man   

  1. College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
  • Received:2018-06-04 Revised:2019-04-15 Online:2019-08-15 Published:2019-08-16

RichHTML

3

PDF (PC)

96

Abstract:

Aiming at the problem of poor precision of artificial detection of existing raw silk cohesiveness and no objective quantitative indicators, a method based on machine vision for detecting the cohesive performance of raw silk was proposed. Firstly, the collected raw silk images were subjected to binarization processing, interference information removal, image filling and raw silk edge detection, and single-pixel raw silk edge feature was obtained. Then, by calculating the linear distance between the upper and lower edge points of the raw silk, the relative change of the diameter of the raw silk was obtained, and the cracked area was determined according to the axial length of the change of the diameter of the raw silk. Finally, the cohesive performance of the raw silk was characterized by the times of raw silk cohesion frictions corresponding to the cracked area greater than 6 mm. The experimental results show that the diameter values of the 200 sets of raw silk measured by the detection method are compared with the diameter values measured by the microscope, and the errors are all within 5%, which satisfies the requirement of raw silk cohesion performance detection.

Key words: cohesion performance, machine vision, number of friction, raw silk, binarization processing

CLC Number: 

  • TP391

Fig.1

Raw silk image acquisition system"

Fig.2

Schematic diagram of the collection area of raw silk friction zone"

Fig.3

Raw silk rubbed image"

Fig.4

Image of threshold segmentation"

Fig.5

Image of noise removal and fill image"

Fig.6

Raw silk image edge detection map"

Fig.7

Raw silk raw diameter measurement comparison chart"

Tab.1

Comparison of original diameter measurement results of four raw silks"

生丝
编号		 显微镜测量
均值/μm		 图像法测量
均值/μm		 像素距离
P0/像素
1# 56.8 57.0 28
2# 55.3 54.9 27
3# 52.8 52.9 26
4# 53.2 52.9 26

Fig.8

Relationship between raw silk cracking area and pixel distance"

Fig.9

Comparison of diameter measurement values of raw silk cracking area"

Fig.10

Number of frictions corresponding to different experimental groups"

[1] 王筠, 于伟东 . 国外生丝抱合性检验方法与比较[J]. 中国纤检, 2007 ( 4):50-54.
WANG Yun, YU Weidong . Cohesion testing method and comparison of raw silk in abroad[J]. China Fiber Inspection, 2007(4):50-54.
[2] 胡祚忠, 叶晶晶, 吴建梅 , 等. 生丝国际标准的研究制定[J]. 丝绸, 2014,51(7):26-31.
HU Zuozhong, YE Jingjing, WU Jianmei , et al. Research and formulation of international standard for raw silk[J]. Journal of Silk, 2014,51(7):26-31.
[3] 孙鸿文, 陈庆官 . 生丝抱合性能检测方法及其装置[J]. 纺织学报, 2005,26(1):80-83.
SUN Hongwen, CHEN Qingguan . Cohesion testing method and equipment of raw silk[J]. Journal of Textile Research, 2005,26(1):80-83.
[4] 孙鸿文, 陈庆官 . 基于虚拟仪器的生丝抱合性能检测系统[J]. 纺织学报, 2005,26(4):54-56.
SUN Hongwen, CHEN Qingguan . Cohesion testing system based on virtual instrumentation[J]. Journal of Textile Research, 2005,26(4):54-56.
[5] 姬建正, 刘建立, 高卫东 , 等. 基于数字图像处理的纱线线密度测量[J]. 纺织学报, 2011,32(10):42-46.
JI Jianzheng, LIU Jianli, GAO Weidong , et al. Measurement of linear density based on digital image processing[J]. Journal of Textile Research, 2011,32(10):42-46.
[6] 孙银银, 潘如如, 高卫东 . 基于数字图像处理的纱线毛羽检测[J]. 纺织学报, 2013,34(6):102-106.
SUN Yinyin, PAN Ruru, GAO Weidong . Detection of yarn hairiness based on digital image processing[J]. Journal of Textile Research, 2013,34(6):102-106.
[7] LI Junjuan, ZUO Baoqi, WANG Chen , et al. A direct measurement method of yarn evenness based on ma-chine[J]. The Journal of Engineered Fibers and Fabrics, 2015,10(4):95-102.
[8] LI Junjuan, WANG Chen, TU Wenxiao , et al. The obtainment and recognition of raw silk defects based on machine vision and image analysis[J]. The Journal of The Textile Institute, 2016,107(3):1-11.
doi: 10.1080/00405000.2014.999478
[9] 李仪芳, 刘景琳 . 基于连通域算法的区域测量[J]. 科学技术与工程, 2008(9):2492-2494.
LI Yifang, LIU Jinglin . Measurement for area based on connected regions arithmetic[J]. Science Technology and Engineering, 2008(9):2492-2494.
[10] 刘万春, 刘建君, 朱玉文 , 等. 一种实时高速的八连通区域填充算法[J]. 计算机应用研究, 2006(6):177-179.
LIU Wanchun, LIU Jianjun, ZHU Yuwen , et al. New high-speed scanline algorithm for filling 8-adjacent connection area[J]. Application Research of Computer, 2006(6):177-179.
[11] 张超, 王志浩, 杨建宇 , 等. 基于Canny算子的农田线状工程地物自动提取方法[J]. 农业机械学报, 2015,46(2):270-275.
ZHANG Chao, WANG Zhihao, YANG Jianyu , et al. Farmland linear project feature auto-extraction method based on canny algorithm[J]. Transaction of the Chinese Society for Agricultural Machinery, 2015,46(2):270-275.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] JIN Shoufeng, LIN Qiangqiang, MA Qiurui, ZHANG Hao. Method for detecting fluff quality of fabric surface based on BP neural network [J]. Journal of Textile Research, 2020, 41(02): 69-76.
[6] JING Junfeng, ZHANG Junyang, ZHANG Huanhuan, SU Zebin. Defect detection on surface of draw texturing yarn packages in gradient space [J]. Journal of Textile Research, 2020, 41(02): 44-51.
[7] JING Junfeng, ZHANG Xingxing. Fiber glass bobbin yarn hairiness detection based on machine vision [J]. Journal of Textile Research, 2019, 40(05): 157-162.
[8] . Comparison between structure and performance of fresh cocoon raw silk and dry cocoon raw silk and identification method thereof#br# [J]. Journal of Textile Research, 2019, 40(03): 32-38.
[9] . Improvement recognition method of vamp′s feature points based on machine vision#br# [J]. Journal of Textile Research, 2019, 40(03): 168-174.
[10] JING Junfeng, GUO Gen. Yarn packages hairiness detection based on machine vision [J]. Journal of Textile Research, 2019, 40(01): 147-152.
[11] . Design of hybrid sensor for detecting broken raw silk [J]. Journal of Textile Research, 2018, 39(09): 134-138.
[12] . On-line yarn cone defects detection system based on machine vision [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(01): 139-145.
[13] . Evaluation and veasurement of precision for raw silk linear density test [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(09): 21-25.
[14] . Characterization on anisotropy of fabric wrinkle recovery [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(06): 42-47.
[15] . Effect of urea on raw silk degumming with papain [J]. JOURNAL OF TEXTILE RESEARCH, 2013, 34(12): 81-0.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] XING Ming-jie;TANG Dian-hua;YU Chong-wen . Effect of three technologic parameters on air-jet spun yarn strength[J]. JOURNAL OF TEXTILE RESEARCH, 2005, 26(1): 49 -51 .
[2] . [J]. JOURNAL OF TEXTILE RESEARCH, 2004, 25(04): 24 -25 .
[3] YANG Ju-ping. Cross-linking flocculants of high molecule and high concentration[J]. JOURNAL OF TEXTILE RESEARCH, 2005, 26(5): 63 -64 .
[4] ZHANG Xi-chang;ZHANG Hai-xia. Effect of twist factor on structure and properties of rotor-spun composite yarns[J]. JOURNAL OF TEXTILE RESEARCH, 2006, 27(6): 67 -70 .
[5] ZHU Zhi-feng;QIAO Zhi-yong. Causes and elimination of the foams within sizing pastes[J]. JOURNAL OF TEXTILE RESEARCH, 2006, 27(7): 86 -89 .
[6] LIN Jian-long;WANG Xiao-bei;GU Xiang. Analysis and design of new model thread-taking-up mechanism of the computerized embroidery machine[J]. JOURNAL OF TEXTILE RESEARCH, 2006, 27(12): 105 -108 .
[7] . [J]. JOURNAL OF TEXTILE RESEARCH, 1995, 16(02): 19 -22 .
[8] . [J]. JOURNAL OF TEXTILE RESEARCH, 1995, 16(02): 46 -48 .
[9] . [J]. JOURNAL OF TEXTILE RESEARCH, 1993, 14(03): 19 -21 .
[10] . [J]. JOURNAL OF TEXTILE RESEARCH, 1987, 8(02): 55 .
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd