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光纤环绕制长度实时测量技术
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摘要
光纤环是干涉型光纤陀螺中传播两相向光波来产生SAGNAC相位差的部件,它由多匝光纤线圈绕制而成。光纤环的绕制长度及平均直径大小是影响光纤陀螺性能的重要因素,在光纤环的生产过程中,对光纤环的绕制长度进行精确测量既是光纤陀螺生产过程中的一个必要环节,也是精度保证的必要条件,因此开展光纤长度实时精确测量技术的研究具有重要的理论意义和工程实用价值。
     论文针对光纤的物理特性和光纤环的绕制特点,利用机器视觉非接触式测量方法,建立了光纤环绕制长度实时精确测量系统,解决了有骨架光纤环绕制长度的实时精确测量问题。
     基于在线测量对测量系统精度和速度的要求,论文从理论研究和实际应用两个方面,对基于视觉测量技术的光纤环绕制长度测量方法进行了深入研究,并对测量方法及相关处理算法进行了实验,验证了测量系统的可行性和实用性。
     论文主要研究内容及成果如下:
     1)根据透镜成像原理,结合光纤环绕制的特点及精确定位光纤成像位置的要求,建立了视觉成像系统;对系统的组成及构建过程中的软件设计、相机选择及固定方式等问题进行了科学分析。
     2)研究了视觉测量过程中的系统标定问题。基于多个自由平面的标定方法,针对光纤成像后形状特征,改进了常用的张正友标定方法,提出了更贴近被测物形状的圆阵列靶标标定摄像机内部参数的方法,且将圆阵列靶标置于与光纤环同轴的平面上标定摄像机外部参数,使得摄像机的成像模型适用于光纤长度测量,提高了光纤环绕制长度测量系统的测量精度。
     3)针对圆阵列靶标模板提出了相对应的标定特征点提取方法,并解决了标定特征点在成像后的坐标位置与其在世界坐标系中位置的对应问题。采用canny边缘检测算子提取平面靶标中圆阵列的轮廓、最小二乘椭圆拟合法求取椭圆的长、短半轴以及Hough变换累计数组方式提取圆阵列靶标上所有标定特征点的坐标。
     4)针对紧密绕制的光纤环成像图像难以用肉眼分辨成像清晰度的问题,在调整镜头焦距判断是否清晰成像的过程中引入图像清晰度评价方法来实时处理图像,确定清晰成像的聚焦位置,指导设置后续的光纤绕制图像采集参数。通过对准平面的合理选取,解决了光纤环绕制过程中随着绕制层数的增加导致光纤环绕制图像的清晰度与视觉系统景深之间的矛盾。
     5)针对光纤环绕制过程的特点,对传统运动目标检测方法进行了改进,使其适用于实时检测跟踪光纤环绕制图像。利用边缘检测算法,得到清晰反映新绕制的光纤位置图像,便于后续计算。
     6)针对光纤材质的特性及绕制过程中出现的绕制缺陷导致长度测量不准确的问题,提出采用光学几何成像方法精确测量光纤环绕制长度的方法。通过测量已知精确直径的光纤环承载主轴的直径,计算系统测量误差,验证了光纤环绕制长度测量方法的正确性。
     在解决以上关键技术问题的基础上,给出了光纤环绕制长度实时测量系统的测量精度为±0.076mm。
Fiber coil is a component of interferometric fiber optic gyroscope (FOG), which is madeof multi-turn fiber coil to strength the SAGNAC phase difference by propagating two wavesin opposite direction. The winding length and the average diameter of fiber coil are theimportant factors to influence the performances of FOG. In the process of fiber coil winding,measuring length of fiber coil winding is not only a necessary step of producing FOG, butalso essential requirement of ensuring FOG accuracy. Therefore, the research on the real-timelength measurement technology of fiber coil winding is of important theoretical and practicalsignificance.
     The non-contact measurement method based on machine vision is applied to measure thelength of fiber in real time according to the physical characteristics of fiber. The measurementsystem is established to measure accurately the length according to the features of backbonefiber coil winding.
     The related algorithms are applied to measure the length of fiber coil winding in realtime based on the accuracy and speed in the process of measurement, the feasibility andpracticability of the measuring system is verified by experiments.
     The main research contents and results are shown as follows:
     1)The visual system was established according to the principle of lens imaging. Thesystem elements was chose by combining the characteristics of the fiber coil winding with therequirement of precisely locating the fiber position of images; then analyzing the systemcomposition and the software and the fixed way of camera.
     2)The calibration problem was researched and realized in the process of visualmeasurement. Based on the multiple freedom plane calibration method, the Zhang zhengyoucalibration method is improved according to the fiber shape in images; The improvedcalibration method is using a circular array target of more close to the measured fiber shape tocalibrate extrinsic parameters of camera, which is suitable for the optical fiber length measurement. In the process of the calibration, the circular array targets will be placed incoaxial with the fiber coil plane, which can improve the measurement accuracy of the system.
     3)The corresponding algorithm was proposed to extract feature points aiming at circulararray target template and solve the location problem of the calibration feature points. Thecanny edge detection operator was used to extract the outline of plane target in the circulararray. The least square ellipse fitting method was used to calculate long and short axis of theellipse. The cumulative array method of Hough transform was used to extract the outline ofplane target in the circular array.
     4)The image clarity evaluation methods were applied on solving image quality problemnot distinguished by the naked eyes in the process of adjusting the focus. The method can beused to determine the focusing position of clear imaging, which can instruct the subsequentimage acquisition. In acquainting fiber imaging, choose the suitable imaging plane to solvethe contraction between increase of the fiber winding lay and the limited the depth of field ofcamera which will cause out-of–focus image of fiber coil winding.
     5) The traditional moving target detection method was improved according to thecharacteristics of optical fiber winding. The improved method is more suitable to detect theprocess of fiber coil winding. The new fiber position is accurately detected in each imagewhich is suit to calculate the length of fiber subsequently.
     6) The geometrical optics imaging method was applied to accurately measure the lengthof fiber winding aiming at the characteristics of fiber and inaccurate length measurementproblem for winding defects. The measurement method is verified by measuring the knowndiameter of the fiber coil bearing spindle.
     The real measurement accuracy of the system achieves±0.076mm.
引文
[1]王巍,干涉型光纤陀螺仪技术[M].北京:中国宇航出版社,2010:33-65.
    [2]张桂才,王巍(译).光纤陀螺仪[M].北京:国防工业出版社,2002:5-21.
    [3]王巍,张桂才,杨清生.光纤陀螺仪及其工程化技术研究[J].导航与控制,2002,1(1):13-17.
    [4] Arditty, H.Jetc. Sagnac effect in fiber gyroseope[J].Optics Letters,1981,6:401-403.
    [5] S.Ezekiel, S.P.Smith. Basic Principle of Fiber-Optic Gyroscopes[J].Academic Press,1994,8(2):621-655.
    [6] Herve Lefevre. The Fiber-Optic Gyroscope[J].Artech House Bostong,1993,27(6):1322-1351.
    [7] V. Vali, R.W.Shorthill. Fiber Ring Interferometer[J].Applied Optics,1976,15(5):1099-1100.
    [8] U. Rich. Fiber-Optic Rotation Sensing With Low Grift[J].Optics Letters,1980,5:170-172.
    [9]秦一帆,胡志雄,葛春风,等.干涉式光纤陀螺的偏振噪声分析[J].光电子技术,2006,26(3):181-184.
    [10]朱荣,张炎华,莫友声.干涉式光纤陀螺的建模与仿真[J].上海交通大学学报,2000,34(11):1492-1496.
    [11]王巍,张桂才.光纤陀螺敏感线圈的温度漂移特性与线圈技术研究[J].中国惯性技术学报,1998,6(l):41-45.
    [12]章毓晋.计算机视觉教程[M].北京:人民邮电出版社,2010:33-68.
    [13] B. G. Batchelor, J. R. Charlier. Machine Vision is not Computer Vision[J].SPIEConference on Machine Vision Systems for Inspection and Metrology VII,1998,3521:2-13.
    [14]迟健男.视觉测量技术[M].北京:机械工业出版社,2011:121-135.
    [15]韩九强.机器视觉技术及应用[M].北京:高等教育出版社,2009:243-269.
    [16]马颂德,张正友.计算机视觉—计算理论与算法基础[M].北京:科学出版社,1998:1-213.
    [17]吴立德.计算机视觉[M].上海:复旦大学出版社,1993:119-208.
    [18]赵鹏.机器视觉研究与发展[M].北京:科学出版社,2012:1-11.
    [19] Y. Aloimonos. Special issue on purposive qualitative active vision[J].CVGIP-IU,1992,56(1):1-20.
    [20]邾继贵,于之靖.视觉测量原理与方法[M].北京:机械工业出版社,2012:1-14.
    [21] T. S. Newman, A. K. Jain. A Survey of Automated Visual Inspection[J].Computer Visionand Image Understanding,1995,61(2):231-262.
    [22] G. Lowe David. Three-dimensional object recognition from single two-dimensionalimages[J].Artificial Intelligence,1987,31(4):355-395.
    [23] D. H. Ballard, C. M. Brown. Principles of Animate Vision[J].Image Understanding,1992,56(1):3-21.
    [24] B. G. Batchilor. Automated visual inspection in industry[J].The Industrial Robot,1978,5:174-176.
    [25] Lahajnar Franci, Bernard Rok, Pernu Franjo, et al. Machine vision system for inspectingelectric plates[J].Computers in Industry,2002,47(1):113-122.
    [26] Hong Deokhwa1, Lee Hyunki1, Kim Minyoung, et al. A3D inspection system for PCBboard by Fusing Moiré Technique and stereo vision algorithm[J].Proceedings of The SICEAnnual Conference,2007:2473-2479.
    [27] Perng Der-Baau, Chou Cheng-Chuan, Lee Shu-Ming. Design and development of a newmachine vision wire bonding inspection system[J].International Journal of AdvancedManufacturing Technology,2007,34(3-4):323-334.
    [28] H. H. Shahabi, T. H.Low, M. M.Ratnam. Notch wear detection in cutting tools usinggradient approach and polynomial fitting[J].International Journal of Advanced ManufacturingTechnology,2009,40(11-12):1057-1066.
    [29]王晓翠,张玉连,麻恒阔.基于图像处理的零件尺寸测量系统的研究[J].航空精密制造技术,2007,6:27-30.
    [30]张宏伟,张国雄,刘书桂,等.零件轮廓测量机安全监测系统的设计[J].机械工程学报,2006,42(2):212-215.
    [31] Zhang Zhisheng, He Boxia, Dai Min, et al. Feature-based sequential partial visionmeasurement method for large scale machine parts[J].Journal of Southeast University(English Edition),2007,23,(4):550-555.
    [32]翟乃斌,苏建,刘玉梅,等.基于计算机视觉的汽车整车尺寸测量系统[J].交通与计算机,2006,24(3):22-26.
    [33]单越康,卫力,毛谦敏.复杂几何形状零件自动检测[J].中国计量学院学报,1997,2:38-47.
    [34] Feng Zhongwei, Xu Chunguang, Xiao Dingguo, et al. In-pipe profile detection usingcircular structured light and its calibration technique[C].Proceedings of the2008IEEEInternational Conference on Information and Automation,2008:1437-1441.
    [35]孙双花,曲兴华,晏彧,等.基于图像测量技术的复杂工件自动检测系统研究[J].制造技术与机床,2007,11:16-20.
    [36]高飞.基于机器视觉技术测量轴类零件尺寸的研究[D].吉林大学,2007.
    [37] S. Qing, W. Di. Instrumentation design and precision analysis of the external diametermeasurement system based on CCD parallel light projection method[J].SPIE,71,3:1-7.
    [38]王庆有,蔡锐,马愈昭,等.采用面阵CCD对大尺寸轴径进行高精度测量的研究[J].光电工程,2003,30(6):36-38.
    [39] J. Y. Chen, B. Y. Lee, K. C. Lee, et al. Development and Implementation of a SimplifiedTool Measuring System[J].Measurement Science Review,2010,10(4):142-146.
    [40] Y. Domae, H. Takauji, S. Kaneko, et al.3D measurement of flexible objects by robustmotion stereo[C].Proceedings of The SICE Annual Conference,2007:740-743.
    [41]蒋蕾,尹业安,常利利.一种基于计算机视觉的织物疵点自动检测方法[J].计算机与现代化,2008,l2:153-159.
    [42]胡亮,段发阶,丁克勤,等.基于线阵CCD钢板表面缺陷在线检测系统的研究[J].计量学报,2005,26(3):200-203.
    [43]王耀南,李树涛,毛建旭.计算机图像处理与识别技术[M].北京:高等教育出版社,2000:36-39.
    [44]张凯丽,刘辉.边缘检测技术的发展研究[J].昆明理工大学学报,2000,25(5):36-39.
    [45]刘超,周激流,何坤.基于Canny算法的自适应边缘检测方法[J].计算机工程与设计,2010,31(18):4036-4039.
    [46] D. Demigny, T. Kamle. A discrete expression of Canny's criteria for step edge detectorPerformances evaluation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(11):1199-1211.
    [47] Kunal Ray. Unsupervised edge detection and noise detection from a single image[J].Pattern Recognition,2013,46:2067-2077.
    [48] C. lopez-Molina, B. De Baets. Multiscale edge detection based on Gaussian smoothingand edge tracking[J].Knowledge-Based Systems,2013,44:101-111.
    [49] Chung-Chia Kang, Wen-June Wang. A novel detection method based on the maximizingobjective function[J].Pattern Recognition,2007,40:609-618.
    [50] Nadia Payet, Sinisa Todorovic. SLEDGE: Sequential Labeling of Image Edges forBoundary Detection[J].Int J Comput Vis,2013,104:15-37.
    [51] E. P. Lyvers, O. R. Mitchell, M. L. Akey, et al. Subpixel measurements using amoment-based edge operator[J].PAMI,1989,11(12):1293-1309.
    [52] V. S. Nalwa, T. O. Binford. On Detecting Edges[J].IEEE Transactions on PAMI,1986,8(6):699-714.
    [53] Jian Ye, Gongkang Fu, Poudel U P. High-accuracy edge detection with blurred edgemodel[J].Image and Vision Computing,2005,23(5):453-467.
    [54]李庆利,张少军,李忠富,等.一种基于多项式插值改进的亚像素细分算法[J].北京科技大学学报,2003,25(3):80-83.
    [55] T. Hermosilla, E. Bermejo, A. Balaguer, et al. Non-linear fourth-order imageinterpolation for subpixel edge detection and localization[J]. Image and Vision Computing,2008,26(9):1240-1248.
    [56] Y. A. Lemeshko, Y. V. Chugui. Precision Dimensional Inspection of Diameters ofCircular Reflecting Cylinders [J].Optoelectronics Instrumentation and Data Processing,2007,43,(3):284-291.
    [57] S. M. Smith, J. M. Brady. SUSAN-A new approach to low level image processing[J].Journal of Computer Vision,1997,23(1):45-78.
    [58] W. Forstner, E. Gulch. A Fast Operator for Detection and Precise Location of DistinctPoints, Corners and Centres of Circular Features[C].Proceedings of IntercommissionConference on Fast Processing of Photogrammetric Data,Interlaken,1987:281-305.
    [59]梁志敏,高洪明,王志江,等.摄像机标定中亚像素级角点检测算法[J].焊接学报,2006,27(2):102-104.
    [60] Y. I. Abdel, H. M. Karara. Direct linear transformation from comparator coordinates intoobject-space coordinates in close-range photogrammetry[C].Proceedings of the ASP/IUSymposium on Close-Range Photogrammetry,1971:1-18.
    [61] R. Y. Tsai. A versatile camera calibration technique for high-accuracy3D machine visionmetrology using off-the-shelf TV cameras and lenses[J].IEEE Journal of Robotics andAutomation,1987,3(4):323-344.
    [62] Zhengyou Zhang. A flexible new technique for camera calibration[J]. IEEE Trans. onPattern Analysis and Machine Intelligence,2000,22(11):1330-1334.
    [63]葛动元.面向精密制造与检测的机器视觉及智能算法研究[M].华南理工大学,2013:342-365.
    [64]吕朝辉,张兆杨,安平.基于神经网络的立体视觉摄像机标定[J].机械工程学报,2003,39(9):93-96.
    [65]王社阳,强文义,陈兴林,等.基于遗传算法的非线性摄像机标定[J].宇航计测技术,2004,24(4):33-38.
    [66] M. Bouchouicha, M. B. Khelifa, W. Puech. A non-linear camera calibration with geneticalgorithm[C].Proceedings of Seventh International Symposium on Signal Processing and ItsApplications,2003:189-192.
    [67]赵晋洪.光纤绕线机精密控制系统的研究[M].浙江大学,2005:297-309.
    [68]杨建华.光纤绕线机控制系统的研制[M].浙江大学,2003:288-321.
    [69]徐德,谭民,李原.机器人视觉测量与控制[M].北京:国防工业出版社,2008:1-29.
    [70]吴福朝.计算机视觉中的数学方法[M].北京:科学出版社,2008:3-149.
    [71]张舞杰,杨义禄,李迪,等.自动影像测量系统关键算法[J].光学精密工程,2007,15(2):294-301.
    [72]邱茂林,马颂德,李毅.计算机视觉中摄像机定标综述[J].自动化学报,2000.26(I):43-55.
    [73] Zhang Zhengyou. Camera Calibration with One-Dimensional Objects[J].IeeeTransactions on Pattern Analysis and Machine Intelligence,2004,7(26):892-899.
    [74]吴文琪,孙增析.机器视觉中的摄像机标定方法综述[J].计算机应用,2005,2:4-6.
    [75]孙军华,刘震,张广军,等.基于柔性立体靶标的摄像机标定[J].光学学报,2009,29(12):3433-3439.
    [76]葛动元,姚锡凡.基于瑞利原理摄像机标定的精度研究[J].计量学报,2009,30(1):11-15.
    [77]储珺,郭卢安政,赵贵花.采用环形模板的棋盘格角点检测[J].光学精密工程,2013,21(1):189-196.
    [78]张旭,李爱国,马孜,等.机器人手眼关系-基坐标系和世界坐标系关系的同时标定[J].控制与决策,2009,24(10):1531-1534.
    [79]叶平,李自亮,孙汉旭.基于立体视觉定位的球形机器人系统研制[J].控制与决策,2012,6:697-703.
    [80]彭飞,陈维荣,冒波波,等.基于Canny边缘检测和聚合接续法的路轨边缘提取方法[J].铁道学报,2012,2:52-57.
    [81]王静,王海亮,向茂生,等.基于非极大值抑制的圆目标亚像素中心定位[J].仪器仪表学报,2012(7):1460-1468.
    [82]唐永鹤,胡谋法,卢焕章.基于自适应滤波的单像素宽形态学边缘检测[J].信号处理,2011,8:1166-1170.
    [83]鲁昌华,韩静,刘春.基于hough变换的角度检测和特征识别[J].电子测量与仪器学报,2005,5:48-52.
    [84]祁宝英.运用hough变换提高直线检测效率[J].计算机系统应用,2012,6:228-231.
    [85]张朝亮,江汉红,张博,等.基于hough变换和harris检测的标尺图像潮位测量[J].计算机科学,2011,3:283-285.
    [86]冈萨雷斯,伍兹.数字图像处理(第3版)[M].北京:电子工业出版社,2011:433-510.
    [87]李奇,冯华君,徐之海,等.数字图像清晰度评价函数研究[J].光子学报,2002,31(6):736-738.
    [88]刘怀,黄建新.基于彩色数字图像处理的自动调焦技术[J].光子学报,2005,34(9):1434-1437.
    [89]张弘.数字图像处理与分析[M].北京:机械工业出版社,2007,190-207.
    [90]余燕飞,郑烇,王嵩,等.基于空间域的图像噪声检测技术[J].计算机应用,2012,32(6):32-35.
    [91]李刚,周彦平.CCD图像传感器件的输出噪声及其处理电路研究[J].检测与制作,2007,4:25-29.
    [92]徐建华.图像处理与分析[M].北京:科学出版社,1992:103-165.
    [93]杨明,宋丽华.改进的快速中值滤波算法在图像去噪中的应用[J].测绘工程,2011,3:65-69.
    [94]牛和明.图像增强、去噪与分割新方法的研究[D].南开大学,2011.
    [95] Lin Hong,Wan, Yi-fei,Jain, A. Fingerprint image enhancement: algorithm andperformance evaluation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,8:777-789.
    [96]韩光松,余志勇. Laplace变换数值反演的参数选择[J].兰州大学学报(自然科学版),2009,z1:118-121.
    [97] Kapur J N,Sahoo P K,Wong A K C. A new method for grey-level picture thresholdingusing the entropy of the histogram[J].Computer Vision, Graphics and ImageProcessing,1985,3:273-285.
    [98]马颂德,张正友.计算机视觉[M].北京:科学出版社,1998:355-367.
    [99]莫建文,马爱红,首照宇,等.基于Brenner函数与新轮廓波变换的多聚焦融合算法[J].计算机应用,2012,32(12):3353-3356.
    [100] R. K. Raney. Synthetic aperture imaging radar and moving targets[J].IEEE Transactionson Aerospace and Electronic Systems,1971,3:499-505.
    [101] J. P. Fienup. Detecting moving targets in SAR imagery by focusing[J].IEEE Trans onAerospace and Electronic Systerns,2001,3:794-809.
    [102] K. Ouchi. On the multilook images of moving targets by synthetic aperture radars[J].IEEE Transactions on Antennas and Propagation,1988,8:823-827.
    [103]赵文哲,秦世引.视频运动目标检测方法的对比分析[J].科技导报,2009,27(10),64-70.
    [104]李秦君,党宏社,王明伟.基于视频的改进背景差分法车辆检测与遮挡分离[J].陕西科技大学学报,2011,29(4):39-42.
    [105]郭贵法,汪仁煌,王欢.改进的同态滤波在指针式仪表图像预处理中的应用[J].广东工业大学学报,2009,3:57-59.
    [106] Guo Chenxia, Yang Ruifeng. The improved defects detection method of optical fiberwinding, OPTIK,2014,125(2):675-678.
    [107]周林,平西建,童莉.基于改进基本图像特征直方图的纹理分类算法[J].系统工程大学信息工程学院,2012,3(6):1272-1277.

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