基于改进图像阈值分割算法的纱线疵点检测

doi:10.13475/j.fzxb.20200700407

纺织学报 ›› 2021, Vol. 42 ›› Issue (03): 82-88.doi: 10.13475/j.fzxb.20200700407

基于改进图像阈值分割算法的纱线疵点检测

李东洁¹^,²(), 郭帅¹, 杨柳¹

1.哈尔滨理工大学自动化学院, 黑龙江哈尔滨 150080
2.哈尔滨理工大学切削加工及制造智能化技术教育部重点实验室, 黑龙江哈尔滨 150080

收稿日期:2020-07-01 修回日期:2020-10-21 出版日期:2021-03-15 发布日期:2021-03-17
作者简介:李东洁(1981—),女,教授,博士。主要研究方向为微纳机器人技术及基于图像的缺陷检测。E-mail: dongjieli2013@163.com。
基金资助:
国家自然科学基金面上项目(51975170);黑龙江省普通高校基本科研业务费专项资金项目(LGYC2018JQ017)

Yarn defect detection based on improved image threshold segmentation algorithm

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

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

摘要/Abstract

摘要：

针对纺织行业纱线疵点检测方法可靠性差、灵敏度低、检测速度低的问题,提出一种基于数字图像处理的纱线疵点判定方法。首先,搭建纱线图像采集系统,完成纱线图像采集;其次,针对纱线边缘信息难处理以及传统双边滤波对椒盐噪声处理效果差的问题,对双边滤波进行改进,改进后的双边滤波可有效保存纱线边缘信息;再者,针对传统阈值分割计算量大、最佳阈值难以寻找的问题,对传统阈值分割算法进行改进,改进的阈值分割算法在保证处理效果的同时提高了整体算法的处理速度;最后,采用亚像素对纱线边缘进行计算,提高了纱线疵点检测的精确度。实验结果验证了算法的有效性及可靠性,该算法在提高精确度的同时将检测速度提高了20%以上,对提高纱线质量检测的准确性具有重要意义。

关键词: 疵点检测, 纱线疵点, 阈值分割, 亚像素点判断, 图像处理

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

中图分类号:

TH74

李东洁, 郭帅, 杨柳. 基于改进图像阈值分割算法的纱线疵点检测[J]. 纺织学报, 2021, 42(03): 82-88.

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.

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参考文献 17

[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.

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参数	数值	参数	数值
a_x/mm	520.547 9	k₁	-0.803 4
a_y/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

疵点类型	传统方法			亚像素方法
疵点类型	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

基于改进图像阈值分割算法的纱线疵点检测

Yarn defect detection based on improved image threshold segmentation algorithm

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