应用光学成像技术检测棉花中异性纤维的研究进展

doi:10.13475/j.fzxb.20190506807

纺织学报 ›› 2020, Vol. 41 ›› Issue (06): 183-189.doi: 10.13475/j.fzxb.20190506807

应用光学成像技术检测棉花中异性纤维的研究进展

董超群¹^,², 杜玉红¹^,²(), 任维佳¹^,², 赵地¹^,²

1.天津工业大学机械工程学院, 天津 300387
2.天津工业大学天津市现代机电装备技术重点实验室, 天津 300387

收稿日期:2019-05-27 修回日期:2019-12-23 出版日期:2020-06-15 发布日期:2020-06-28
通讯作者: 杜玉红
作者简介:董超群(1995—),男,硕士生。主要研究方向为图像处理及模式识别。
基金资助:
国家自然科学基金项目(51205288);国家自然科学基金项目(U1733108);天津市自然科学基金项目(17JCYBJC19400)

Research progress in optical imaging technology for detecting foreign fibers in cotton

DONG Chaoqun¹^,², DU Yuhong¹^,²(), REN Weijia¹^,², ZHAO Di¹^,²

1. School of Mechanical Engineering, Tiangong University, Tianjin 300387, China
2. Tianjin Key Laboratory of Advanced Mechatronics Equipment Technology, Tiangong University, Tianjin 300387, China

Received:2019-05-27 Revised:2019-12-23 Online:2020-06-15 Published:2020-06-28
Contact: DU Yuhong

摘要/Abstract

摘要：

为进一步提高棉花中异性纤维的检出率,针对光学成像技术在异性纤维检测中的应用情况进行探究,通过阐述紫外光、X射线、线激光、偏振光、红外光和高光谱成像技术的原理和检测效果,分析了各成像方法的优势及局限性。归纳总结了现有研究中存在的问题和不足,认为目前不同种类异性纤维检测适用的成像方法不同,无法同时检测出全部种类的异性纤维;而且多相机成像方案和相机分辨率的提高增加了图像冗余信息,影响了检测速度;同时大部分检测方法仅在实验室条件下得到验证,缺乏实际生产环境的检验。最后指出未来会以多相机多光源成像方案为主,减少图像信息冗余,合理选择光源的种类、数量、功率和安装方式,开发成像系统参数自动调整系统。

关键词: 棉花异性纤维, 光源组合, 光谱分析, 光学成像, 检出率

Abstract:

In order to further improve the detection rate of foreign fibers in cotton, the application of optical imaging technology in foreign fiber detection was explored. The principle and detection effect of ultraviolet, X-ray, linear laser, polarized light, infrared light and hyper-spectral imaging technology were evaluated and analyzed on the advantages and limitations of the various imaging methods, and the existing problems and deficiencies in the current research were summarized. It is considered that different imaging methods should be applied to detect different types of foreign fibers, and it is not possible to detect all types of foreign fibers at the same time. Moreover, the multi-camera imaging scheme and the improvement of camera resolution are found to increase the redundant information of images and affect the detection speed. At the same time, most of the testing methods are only verified under laboratory conditions and are short of verification in actual production environment. It is pointed out that the future research should focus on multi-camera multi-light source imaging scheme, and should work to reduce the redundancy of image information. Light sources should be reasonably selected on type, quantity, power and installation mode, and an automatic parameter adjustment system should be developed for imaging systems.

Key words: cotton foreign fiber, light source combination, spectral analysis, optical imaging, recognition ratio

中图分类号:

TS111.9

董超群, 杜玉红, 任维佳, 赵地. 应用光学成像技术检测棉花中异性纤维的研究进展[J]. 纺织学报, 2020, 41(06): 183-189.

DONG Chaoqun, DU Yuhong, REN Weijia, ZHAO Di. Research progress in optical imaging technology for detecting foreign fibers in cotton[J]. Journal of Textile Research, 2020, 41(06): 183-189.

图/表 1

表1

参考文献 41

[1]	叶戬春. 棉纺企业对棉花质量的需求[J]. 中国纤检, 2018(1):24-29.
	YE Jianchun. A requirement of textile enterprises for cotton quality[J]. China Fiber Inspection, 2018(1):24-29.
[2]	丁纪文. 棉花异性纤维的产生、危害与防治[J]. 中国棉花加工, 2017(2):47-48.
	DING Jiwen. Production, harm and prevention of foreign fibers in cotton[J]. China Cotton Processing, 2017(2):47-48.
[3]	盖文桥, 徐雷, 丁曰东. 浅析棉花异性纤维形成的原因、分类、危害和解决措施[J]. 中国纤检, 2017(9):85-86.
	GAI Wenqiao, XU Lei, DING Yuedong. Causes, classification, harm and solutions of foreign fibers in cotton[J]. China Fiber Inspection, 2017(9):85-86.
[4]	周建, 潘如如, 高卫东. 机器视觉在纺织中的应用现状与发展趋势[J]. 棉纺织技术, 2019,47(2):15-17.
	ZHOU Jian, PAN Ruru, GAO Weidong. Applicaton status and development trend of machine vision in textile[J]. Cotton Textile Technology, 2019,47(2):15-17.
[5]	张云, 许江淳, 王志伟, 等. 基于机器视觉的棉花异性纤维检测技术优化研究[J]. 中国农机化学报, 2018,39(9):61-65.
	ZHANG Yun, XU Jiangchun, WANG Zhiwei, et al. Optimization of cotton heterosexual detection technology based on machine vision[J]. Journal of Chinese Agricultural Mechanization, 2018,39(9):61-65.
[6]	袁昊, 刘忠强, 陈大华. 紫外光在环保处理中的应用研究[J]. 灯与照明, 2018,42(4):38-41, 45.
	YUAN Hao, LIU Zhongqiang, CHEN Dahua. Application of ultraviolet light in environmental protection treatment[J]. Light & Lighting, 2018,42(4):38-41, 45.
[7]	ZHOU Fei, DING Tianhuai. Detection of cotton lint trash within the ultraviolet: visible spectral range[J]. Applied Spectroscopy, 2010,64(8):936-941. pmid: 20719059
[8]	MUSTAFIC A, LI C U, HAIDEKKER M. Blue and UV led-induced fluorescence in cotton foreign matter[J]. Journal of Biological Engineering, 2014,8:29. pmid: 25926886
[9]	郑鹏. 基于多模式分类算法的棉花异性纤维自动检测系统的设计与实现[D]. 洛阳:河南科技大学, 2017: 3-26.
	ZHENG Peng. The design and implementation of automatic detection system of foreign fiber in cotton based on multi pattern classification algorithm[D]. Luoyang: Henan University of Science and Technology, 2017: 3-26.
[10]	杜玉红, 杨程午, 蒋秀明, 等. 应用聚类神经网络的异纤检测多类光源优化设计[J]. 纺织学报, 2017,38(10):104-112.
	DU Yuhong, YANG Chengwu, JIANG Xiuming, et al. Optimization design of multi-light source for foreign fiber detection based on clustering neural network[J]. Journal of Textile Research, 2017,38(10):104-112.
[11]	束月霞, 赵丽丽, 蒋皆恢, 等. X射线发光光学断层成像的研究进展[J]. 科学通报, 2017,62(33):3838-3850.
	SHU Yuexia, ZHAO Lili, JIANG Jiehui, et al. Research progress of X-ray luminescence optical tomography[J]. Chinese Science Bulletin, 2017,62(33):3838-3850.
[12]	PAVANI S K, PRICE J R, MERIAUDEAU F, et al. Machine vision applications in industrial inspection Ⅻ-segmentation and classification of four common cotton contaminants in X-ray microtomographic images[J]. Proceedings of SPIE, 2004,5303:1.
[13]	PAI A, SARI S H, HEQUET E F. Recognition of cotton contaminants via X-ray microtomographic image analy-sis[C]// Industry applications conference. PA: IEEE, 2002: 77-85.
[14]	孙玉博, 熊玲玲, 张普, 等. 半导体激光器光束匀化系统的光学设计[J]. 红外与激光工程, 2019,48(12):1-7.
	SUN Yubo, XIONG Lingling, ZHANG Pu, et al. Optical design of laser diode beam-homogenizing system[J]. Infrared and Laser Engineering, 2019,48(12):1-7.
[15]	韦平, 张林, 刘翔, 等. 籽棉中异性纤维的双光源成像检测方法[J]. 纺织学报, 2017,38(4):32-38.
	WEI Ping, ZHANG Lin, LIU Xiang, et al. Detecting method of foreign fibers in seed cotton using double illumination imaging[J]. Journal of Textile Research, 2017,38(4):32-38.
[16]	HUA Caijian, SU Zhenwei, QIAO Li, et al. White foreign fibers detection in cotton using line laser[J]. Transactions of the Chinese Society for Agricultural Machinery, 2012,43(2):181-185.
[17]	张林, 韦平, 伍剑波, 等. 基于线激光与LED的棉花中异性纤维检测方法[J]. 农业工程学报, 2016,32(15):289-293.
	ZHANG Lin, WEI Ping, WU Jianbo, et al. Detection method of foreign fibers in cotton based on illumination of linelaser and LED[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(15):289-293.
[18]	刘锋, 苏真伟, 乔丽. 基于线激光截面成像的棉花白色异性纤维检测方法[J]. 农业机械学报, 2013,44(3):215-218,256.
	LIU Feng, SU Zhenwei, QIAO Li. Linear laser detecting method of white foreign fibers in cotton based on sample cross-section imaging[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013,44(3):215-218,256.
[19]	何晓昀, 韦平, 张林, 等. 基于深度学习的籽棉中异性纤维检测方法[J]. 纺织学报, 2018,39(6):131-135.
	HE Xiaoyun, WEI Ping, ZHANG Lin, et al. Detection method of foreign fibers in seed cotton based on deep-learning[J]. Journal of Textile Research, 2018,39(6):131-135.
[20]	褚君浩, 胡志高. 红外偏振效应和偏振遥感研究进展[J]. 遥感学报, 2018,22(6):926-934.
	CHU Junhao, HU Zhigao. Recent progress on infrared polarization effect and polarization remote applica-tions[J]. Journal of Remote Sensing, 2018,22(6):926-934.
[21]	韩勇, 赵开春, 尤政. 快速旋转式偏振成像探测装置的设计[J]. 光学精密工程, 2018,26(10):2345-2354.
	HAN Yong, ZHAO Kaichun, YOU Zheng. Developement of rapid rotary polarization imaging detection devices[J]. Optics and Precision Engineering, 2018,26(10):2345-2354.
[22]	PENG Bo, HUANG Shaling, LI Dongjie. Detection of colorless plastic contaminants hidden in cotton layer using chromatic polarization imaging[J]. Chinese Optics Letters, 2015,13(9):81-85.
[23]	贾小秋. 超越M型异纤分拣机的应用[J]. 棉纺织技术, 2016,44(8):48-51.
	JIA Xiaoqiu. Application of chaoyue m foreign fiber cleaner[J]. Cotton Textile Technology, 2016,44(8):48-51.
[24]	张晨, 孙世磊, 石文轩, 等. 基于嵌入式系统的异纤清除机设计与试验[J]. 农业机械学报, 2017,48(8):43-52.
	ZHANG Chen, SUN Shilei, SHI Wenxuan, et al. Design and test of foreign fiber removal machine based on embedded system[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017,48(8):43-52.
[25]	张华林. 光的弹性效应在异性纤维检测中的运用[J]. 科技创新与应用, 2016(27):77.
	ZHANG Hualin. The application of the elastic effect of light in the detection of foreign fibers[J]. Technology Innovation and Application, 2016(27):77.
[26]	汤菲菲, 姚燕, 邵玉涛, 等. 基于理论吸收光谱的棉纤维波长筛选方法[J]. 江苏农业科学, 2019,47(7):207-209.
	TANG Feifei, YAO Yan, SHAO Yutao, et al. Cotton fiber wavelength screening method based on theoretical absorption spectrum[J]. Jiangsu Agricultural Sciences, 2019,47(7):207-209.
[27]	王泓智, 孙玉彬. 傅里叶红外光谱技术在风电机组化学监督中的应用分析[J]. 风力发电, 2018(2):38-42.
	WANG Hongzhi, SUN Yubin. Application analysis of fourier transform infrared spectroscopy in chemical supervision of wind turbines[J]. Wind Power, 2018(2):38-42.
[28]	HIMMELSBACH D S, HELLGETH J W, MCALISTER D D. Development and use of an attenuated total reflectance/fourier transform infrared (ATR/FT-IR) spectral database to identify foreign matter in cotton[J]. Journal of Agricultural and Food Chemistry, 2006,54(20):7405-7412. pmid: 17002401
[29]	CINTRÓN M S, RODGERS J E. Identification of common cotton contaminants using an ftir microscope with a focal plane array detector[J]. AATCC Journal of Research, 2017,4(6):12-17.
[30]	LOUDERMILK J B, HIMMELSBACH D S, BARTON F E, et al. Novel search algorithms for a mid-infrared spectral library of cotton contaminants[J]. Applied Spectroscopy, 2008,62(6):661-670. pmid: 18559154
[31]	ALLEN A. Preliminary fourier-transform infrared spectroscopy analysis of cotton trash.[J]. Journal of Cotton Science, 2007,11(1):68.
[32]	周莹, 徐惠荣, 应义斌. 近红外技术在自然纺织纤维品种鉴别及成分预测上的应用[J]. 光谱学与光谱分析, 2008,28(12):2804-2807.
	ZHOU Ying, XU Huirong, YING Yibin. Nir analysis of textile natural raw material[J]. Spectroscopy and Spectral Analysis, 2008,28(12):2804-2807.
[33]	BÖHMER S, HELMUT B, VOLKER K, et al. Nirmeasurement system to detect foreign matter in row cotton[J]. Technisches Messen, 2008,75(6):406-412.
[34]	CHURCH J S, O'NEILL J A, WOODHEAD A L. Detection of fibrilated polymeric contaminants in wool and cotton yarns[J]. Applied Spectroscopy, 1998,52(8):1039-1046.
[35]	ALCHANATIS V, RIDEL L, HETZRONI A, et al. Weed detection in multi-spectral images of cotton fields[J]. Computers and Electronics in Agriculture, 2005,47(3):243-260.
[36]	李盛阳, 刘志文, 刘康, 等. 航天高光谱遥感应用研究进展(特邀)[J]. 红外与激光工程, 2019,48(3):9-23.
	LI Shengyang, LIU Zhiwen, LIU Kang, et al. Advances in application of space hyperspectral remote sen-sing (invited)[J]. Infrared and Laser Engineering, 2019,48(3):9-23.
[37]	刘巍, 史勇, 田海清, 等. 高光谱反射、透射和反透射成像模式的皮棉杂质检测方法研究[J]. 现代纺织技术, 2019,27(5):44-49.
	LIU Wei, SHI Yong, TIAN Haiqing, et al. Detection method for cotton impurities based on reflection, transmission and reflection-transmission hyperspectral imaging[J]. Advanced Textile Technology, 2009,27(5):44-49.
[38]	JIANG Yu, LI Changying. Mrmr-based feature selection for classification of cotton foreign matter using hyperspectral imaging[J]. Computers and Electronics in Agriculture, 2015,119:191-200.
[39]	MUSTAFIC A, JIANG Y, LI C Y. Cotton contamination detection and classification using hyperspectral fluorescence imaging[J]. Textile Research Journal, 2016,86(15):1574-1584.
[40]	郭俊先, 李雪莲, 黄华, 等. 基于可见短波近红外高光谱图像的梳棉杂质关键波长的优选[J]. 新疆农业科学, 2016,53(2):352-358.
	GUO Junxian, LI Xuelian, HUANG Hua, et al. Wavelengths selection of trashes detection in combed cotton using hyper-spectral imaging at visible and short-wave near infrared wavelength range[J]. Xinjiang Agricultural Sciences, 2016,53(2):352-358.
[41]	侯榜焕, 姚敏立, 贾维敏, 等. 空谱结构保持的高光谱图像分类[J]. 红外与激光工程, 2017,46(12):301-308.
	HOU Banghuan, YAO Minli, JIA Weimin, et al. Hyperspectral image classification based on spatial-spectral structure preserving[J]. Infrared and Laser Engineering, 2017,46(12):301-308.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

生产企业	机型	成像系统组成		检测指标
生产企业	机型	相机	光源	检测指标
瑞士Jossi公司	Mpix	2个线彩色CCD相机(3 000像素)	白光+紫外光光源	彩色和部分白色异性纤维
德国Trützschler公司	SCFO	2个线彩色CCD相机(2 048像素)	荧光灯	彩色异性纤维
瑞士Rieter公司	Jossi	2个线彩色CCD相机	白光+紫外光光源	彩色和部分白色异性纤维
瑞士Loepfe公司	Cotton sorter	4个线彩色CCD相机(2 592像素)	白光光源	彩色异性纤维
比利时Barco公司	Barco-CS	2个线彩色CCD相机(2 592像素)	白光光源	80%彩色异性纤维
比利时Barco公司	Compact	2个线彩色CCD相机(3 000像素)	荧光灯	1 cm²彩色异性纤维
无锡恒久电器科技公司	CCH系列	2个线彩色CCD相机(2 098像素)	荧光灯	85%彩色异性纤维(长度>1 cm)
北京大恒图像视觉有限公司	超越系列	3个线彩色CCD相机+ 1个黑白CCD相机	白光+紫外光光源 (带有偏振片)	85% 白色和彩色(长度>0.5 cm)
北京经纬纺机新技术公司	JWF001	2个线彩色CCD相机(4 096像素)	荧光灯	90%深色异性纤维(>0.5 cm²)
北京经纬纺机新技术公司	JWF0011E	2个线彩色CCD相机	白光+紫外光光源	长度为1 mm全色谱丝状异性纤维
大连贵友科技公司	CS系列	线彩色CCD相机(2 098像素)	可见光+紫外光光源	85%白色和彩色异性纤维(>1 mm)
上海奥达光电子科技有限公司	NDFC	线彩色CCD相机(2 098像素)	可见光+紫外光光源	可检测彩色和部分白色异性纤维

应用光学成像技术检测棉花中异性纤维的研究进展

Research progress in optical imaging technology for detecting foreign fibers in cotton

RichHTML

PDF (PC)

摘要/Abstract

引用本文

使用本文

图/表 1

参考文献 41

相关文章 4

Metrics

本文评价

推荐阅读 0

[1]	李佳平沈国康欧耀明孟想辛斌杰. 应用连续投影算法及最小二乘支持向量机的单组分纺织品识别[J]. 纺织学报, 2018, 39(08): 46-51.
[2]	曾春慧齐鲁. 还原法与金属盐法结合溶解废旧羊毛[J]. 纺织学报, 2011, 32(11): 12-16.
[3]	丁友超;曹锡忠;蔡建和;周佳;钱凯. 纺织品中有害分散染料的细粒径柱液相色谱-光谱-质谱检测技术[J]. 纺织学报, 2011, 32(1): 89-95.
[4]	张涛;鲍文斌;俞建勇. 竹浆纤维鉴别方法的研究[J]. 纺织学报, 2004, 25(05): 28-29.