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

RichHTML

21

PDF (PC)

174

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"

[1] ZHANG B, TANG C. Study on detection method of low contrast fabric defects image based on regional variation saliency[C] //2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).Chengdu: Institute of Electrical and Electronics Engineers, 2019: 1103-1106.
[2] CHENG L, ZHAO L Y, CHEN L, et al. Digital image processing of cotton yarn seriplane[C] //2010 International Conference on Computer and Information Application.Tianjin: Institute of Electrical and Electronics Engineers, 2010: 274-277.
[3] CARVALHO V, CARDOSO P, BELSLEY M, et al. Development of a yarn evenness measurement and hairiness analysis system[C] // IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics. Paris: Institute of Electrical and Electronics Engineers, 2006: 3621-3626.
[4] 徐国香. UST-4条干仪的测试及应用[J]. 毛纺科技, 2007,35(9):54-57.
XU Guoxiang. Test and application of UST-4 dry instrument[J]. Wool Textile Journal, 2007,35(9):54-57.
[5] 盛国俊. 光电式纱线质量检测系统[D]. 北京: 清华大学, 2009: 2-4.
SHENG Guojun. Photo electric yarn quality detection system[D]. Beijing:Tsinghua University, 2009: 2-4.
[6] OZKAYA Y, ACAR M, JACKSON M. Digital image processing and illumination techniques for yarn characterization[J]. Journal of Electronic Imaging, 2005,14(2):1-13.
doi: 10.1117/1.1900134 pmid: 17235365
[7] GUHA A, AMARNATH C, PATERIA S, et al. Measurement of yarn hairiness by digital image processing[J]. Journal of The Textile Institute, 2010,101(3):214-222.
[8] 章国红, 辛斌杰, 张瑞. 基于图像分析技术的纱线黑板条干检测[J]. 棉纺织技术, 2016,44(12):19-24.
ZHANG Guohong, XIN Binjie, ZHANG Rui. Yarn strip detection based on image analysis technology[J]. Cotton Textile Technology, 2016,44(12):19-24.
[9] FABIJANSKA A, JACKOWSKA-STRUMILLO L. Image processing and analysis algorithms for yarn hairiness determination[J]. Machine Vision and Applications, 2012,23(3):527-540.
[10] VLADIMIR G, IONOV Evgen, NAING Lin Aung, Automatic detection and classification of weaving fabric defects based on digital image processing[C] //2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering. Saint Petersburg: Institute of Electrical and Electronics Engineers, 2019: 2218-2221.
[11] ZHANG Z Y. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(11):1330-1334.
[12] LENG X, JI K, XING X. Hybrid bilateral filtering algorithm based on edge detection[J]. IET Image Processing, 2016,10(11):809-816.
[13] LI S Y, XU B G, FU H, et al. A two-scale attention model for intelligent evaluation of yarn surface qualities with computer vision[J]. Journal of The Textile Institute, 2018,109(6):798-812.
[14] LI S Y, FENG J, XU B J, et al. Integrated digital system for yarn surface quality evaluation using computer vision and artificial intelligence[J]. IPCV, 2012,1:472-476.
[15] LI Z J, PAN R R, GAO W D. Formation of digital yarn black board using sequence images[J]. Textile Research Journal, 2016,86(6):593-603.
[16] 迟开龙, 潘如如, 刘基宏, 等, 基于数字图像处理的纱线条干均匀度检测初探[J]. 纺织学报, 2012,33(12):19-24.
CHI Kailong, PAN Ruru, LIU Jihong, et al. Primary discussion on detection of yarn evenness based on digital image processing[J]. Journal of Textile Research, 2012,33(12):19-24.
[17] 李变变, 李忠健, 潘如如, 等. 纱线条干均匀性序列图像测量方法[J]. 纺织学报, 2016,37(11):26-31.
LI Bianbian, LI Zhongjian, PAN Ruru, et al. Measurement of yarn evenness using sequence images[J]. Journal of Textile Research, 2016,37(11):26-31.
[1] 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.
[2] 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.
[3] ZHOU Wenming, ZHOU Jian, PAN Ruru. Yarn-dyed fabric defect detection based on context visual saliency [J]. Journal of Textile Research, 2020, 41(08): 39-44.
[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] ZHANG Zhengye, XIN Binjie, DENG Na, CHEN Yang, XING Wenyu. Research and application of algorithm for measuring hemp fiber cross-sectional parameters based on boundary tracking [J]. Journal of Textile Research, 2020, 41(02): 39-43.
[6] WU Yilun, LI Zhongjian, PAN Ruru, GAO Weidong, ZHANG Ning. Weft knitted fabric appearance simulation using colored spun yarn image [J]. Journal of Textile Research, 2019, 40(06): 111-116.
[7] HUANG Jiajun, KE Wei, WANG Jing, DENG Zhongmin. Color shading detection and rating system for denim based on computer vision [J]. Journal of Textile Research, 2019, 40(05): 163-169.
[8] WANG Wendi, XIN Binjie, DENG Na, LI Jiaping, LIU Ningjuan. Identification and application of yarn hairiness using adaptive threshold method under single vision [J]. Journal of Textile Research, 2019, 40(05): 150-156.
[9] CAI Yichao, ZHOU Xiao, SONG Mingfeng, MOU Xin'gang. Defect detection of cheese yarn based on multi-scale multi-direction template convolution [J]. Journal of Textile Research, 2019, 40(04): 152-157.
[10] XU Yang, ZHU Zhichao, SHENG Xiaowei, YU Zhiqi, SUN Yize. Improvement recognition method of vamp's feature points based on machine vision [J]. Journal of Textile Research, 2019, 40(03): 168-174.
[11] DU Shuai, LI Yueyang, WANG Mengtao, LUO Haichi, JIANG Gaoming. Fabric defect detection based on improved local adaptive contrast method [J]. Journal of Textile Research, 2019, 40(02): 38-44.
[12] . Detection and evaluation on yarn hairiness of blackboard with image processing [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(08): 144-149.
[13] . Position recognition of spinning yarn breakage based on convolution neural network [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(06): 136-141.
[14] . Detecting method of foreign fibers in seed cotton based on deep-learning [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(06): 131-135.
[15] . Spinning breakage detection based on optimized hough transform [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(04): 36-41.
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