An image mosaic technique with non-overlapping regions based on microscopic vision in precision assembly

Microscopic vision has been widely applied in precision assembly.To achieve sufficiently high resolution in measurements for precision assembly when the sizes of the parts involved exceed the field of view of the vision system,an image mosaic technique must be used.In this paper,a method for constructing an image mosaic with non-overlapping areas with enhanced efficiency is proposed.First,an image mosaic model for the part is created using a geometric model of the measurement system installed on a X-Y-Z precision stages with high repeatability,and a path for image acquisition is established.Second,images are captured along the same path for a specified calibration plate,and an entire image is formed based on the given model.The measurement results obtained from the specified calibration plate are utilized to identify mosaic errors and apply compensation for the part requiring measurement.Experimental results show that the maximum error is less than 4μm for a camera with pixel equivalent 2.46μm,thereby demonstrating the accuracy of the proposed method.This image mosaic technique with non-overlapping regions can simplify image acquisition and reduce the workload involved in constructing an image mosaic.

Accurate Measurement Method for Tube's Endpoints Based on Machine Vision

Tubes are used widely in aerospace vehicles, and their accurate assembly can directly affect the assembling reliability and the quality of products. It is important to measure the processed tube＇s endpoints and then fix any geometric errors correspondingly. However, the traditional tube inspection method is time-consuming and complex operations. Therefore, a new measurement method for a tube＇s endpoints based on machine vision is proposed. First, reflected light on tube＇s surface can be removed by using photometric linearization. Then, based on the optimization model for the tube＇s endpoint measurements and the principle of stereo matching, the global coordinates and the relative distance of the tube＇s endpoint are obtained. To confirm the feasibility, ll tubes are processed to remove the reflected light and then the endpoint＇s positions of tubes are measured. The experiment results show that the measurement repeatability accuracy is 0.167 mm, and the absolute accuracy is 0.328 ram. The measurement takes less than 1 min. The proposed method based on machine vision can measure the tube＇s endpoints without any surface treatment or any tools and can realize on line measurement.

Machine Vision Based Measurement of Dynamic Contact Angles in Microchannel Flows

When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and sealed with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB environment. Images are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.

Visual interactive image clustering:a target-independent approach for configuration optimization in machine vision measurement

Machine vision measurement(MVM)is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control.The result of MVM is determined by its configuration,especially the lighting scheme design in image acquisition and the algorithmic parameter optimization in image processing.In a traditional workflow,engineers constantly adjust and verify the configuration for an acceptable result,which is time-consuming and significantly depends on expertise.To address these challenges,we propose a target-independent approach,visual interactive image clustering,which facilitates configuration optimization by grouping images into different clusters to suggest lighting schemes with common parameters.Our approach has four steps:data preparation,data sampling,data processing,and visual analysis with our visualization system.During preparation,engineers design several candidate lighting schemes to acquire images and develop an algorithm to process images.Our approach samples engineer-defined parameters for each image and obtains results by executing the algorithm.The core of data processing is the explainable measurement of the relationships among images using the algorithmic parameters.Based on the image relationships,we develop VMExplorer,a visual analytics system that assists engineers in grouping images into clusters and exploring parameters.Finally,engineers can determine an appropriate lighting scheme with robust parameter combinations.To demonstrate the effiectiveness and usability of our approach,we conduct a case study with engineers and obtain feedback from expert interviews.

Employment of predictive search algorithm in digital image correlation

A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference image scheme was used to update the reference image and to decrease the computation time when the displacement was larger than a certain number.In this way,the search range and computational complexity were cut down,and less EMS memory was occupied.The capability of proposed search algorithm was then verified by the results of both computer simulation and experiments.The results showed that the algorithm could improve the efficiency of correlation method and satisfy the accuracy requirement for practical displacement measuring.

电力变压器关键尺寸视觉检测方法及其缩比模型验证

电力变压器的关键尺寸测量是其装配制造、运输安装过程中重要一环,现有测量方法操作繁琐且效率较低。为此,本文提出了一种适用于110 kV油浸式电力变压器关键尺寸视觉检测方法,该方法利用YOLO目标检测算法、Grabcut图像分割算法实现关键组部件的智能检测与分割,然后基于双目立体视觉原理实现套管相间距离及变压器最大截面外形尺寸等关键尺寸的测量。本文搭建了110 kV油浸式电力变压器外观缩比模型,试验分析了拍摄距离和角度等因素对电力变压器关键尺寸视觉检测的影响。结果表明,本文基于缩比模型试验实现了在不同拍摄距离与角度下变压器关键尺寸的视觉检测,验证了该方法的有效性。本文方法可以为110 kV油浸式电力变压器现场尺寸测量提供参考。

基于机器视觉的刚性罐道接头缝隙宽度测量算法

针对罐道结构复杂、空间狭小、传统检测系统不便于安装,容易导致接头缝隙测量困难问题,提出一种基于机器视觉的非接触式测量接头缝隙宽度的方法。使用高斯滤波和CLAHE算法,去除噪声并提高图像对比度,经过阈值分割、图像形态学操作提取接头缝隙边缘特征;通过孤立点消除和透视变换去除图像畸变,使用SKE-骨架提取算法减少接头缝隙特征像素宽度,通过霍夫变换和最小二乘法计算接头缝隙宽度。测量了4~12mm接头缝隙宽度,结果表明:该方法测量误差控制在0.8 mm以内,为立井提升系统安全稳定运行提供保障。

基于IMAQ Vision的小模数直齿圆柱齿轮测量方法研究

本文以机器视觉技术为基础,研究了基于IMAQ Vision的渐开线小模数直齿圆柱齿轮的测量技术,介绍了对渐开线小模数直齿圆柱齿轮进行非接触测量的方法,通过利用IMAQ Vision软件进行图像采集、预处理、边缘检测、边缘拟合、系统标定等,得到被测齿轮参数的精确结果,研究了图像法齿轮几何量参数的测量方法,并对测量精度进行了分析。使用分辨率为768×576像素的CCD摄像头以及图像采集卡PCI-1409对分度圆直径为6.4mm的直齿圆柱齿轮进行了测量实验,并与万工显测量值进行了比对,测量精度达到±2μm,从而证明了该方法的可行性。

基于转像激光立体视觉的油管接箍内螺纹参数检测

油管接箍是油田钻井用重要连接工具,其内螺纹质量对于油气安全开采至关重要。针对现有油管接箍内螺纹接触式检测方法效率低、磨损大、光谱共焦等非接触式方法测量可达性差的问题,提出基于转像激光立体视觉的油管接箍内螺纹参数检测系统与方法,利用激光和反射镜实现内螺纹牙型轮廓的强化和转角窥视,借助已标定的相机模型参数,通过立体视觉和旋转进给重构纵截面中双侧牙型的三维点云,经进一步处理,从三维点云中计算出内螺纹螺距、齿高和锥度三项几何参数。依据美国石油协会(API)标准规定的参数容限范围实现油管接箍内螺纹质量的判定,研制出视觉系统,并与传统量具在相同工况下测量27/8与31/2英寸两种规格的油管内螺纹几何参数,结果表明,提出的视觉系统可实现内螺纹多项参数的一体化高精、高效测量,满足工业现场需求。

非匀速条件下车辆底盘超近距成像测量方法

针对车辆非匀速条件下轴距视觉测量及轴型识别所面临的超近距、大幅面、低畸变成像及视觉测量问题,提出一种适用于非匀速条件下的车辆底盘结构超近距成像测量方法。利用鱼眼相机采集运动车辆底盘的超广角图像序列,通过一种改进的多采样点圆拟合方法实现图像感兴趣区域的提取。建立三维等距成像模型对鱼眼图像进行畸变校正。针对车辆非匀速行驶带来的视频帧重叠度差异,采用基于图像内容的重叠度分析技术,实现了多步分层的关键帧提取,有效避免了车速不稳定对车辆底盘结构测量的影响。现场实验结果表明:该方法可在非匀速条件下,以20~50 cm的成像距离获得清晰、低畸变的完整车辆底盘图像,车辆轴距测量误差均值约为5%。

基于机器视觉技术的激光焊机支撑块零件测绘的研究

机器视觉测量技术具有非接触、全视场测量、高精度和自动化程度高的特点,特别适用于轻微破损零件的测绘及后续修复。本文通过选用合适的光源、镜头和相机,搭建了机器视觉测量系统,对激光焊机设备支撑块零件4个面进行了视觉图像采集,并通过康耐视VisionPro软件对图像进行处理与分析,实现了对该零件整体尺寸的视觉精准测量。在此基础上,采用SolidWorks软件,完成支撑块的三维模型设计,为后续零件的加工与替换奠定技术基础。

宽基线立体视觉远距离目标位姿测量

立体视觉测量作为被动探测技术的一种,广泛应用于位姿测量领域。针对传统立体视觉在远距离环境下目标位姿测量受限的问题,提出一种宽基线立体视觉远距离目标位姿测量方法。基于三点定面测量原理,构建位姿测量数学模型,采用相机参数解耦策略,对测量系统所需参数进行标定。设计远距离场景下易于识别的合作目标,利用两台相机对目标进行成像,通过计算合作目标中心点的三维空间坐标,完成远距离环境下目标位姿的解算。实验结果表明,在75~85 m距离下,该方法对目标间相对位姿测量精度优于0.5%,X和Y方向均方根误差优于7.3 mm,Z方向均方根误差优于22.6 mm,可有效适用于远距离下的目标位姿测量应用。

基于机器视觉技术的高精度光学仪器表面动态变形测量研究

伴随着光学仪器精度的逐渐提高,其表面动态变形事件发生频率也呈现急剧增加的趋势,是影响与制约光学仪器发展与应用的主要因素之一。为了满足光学仪器的应用需求,提出基于机器视觉技术的高精度光学仪器表面动态变形测量方法。采用机器视觉设备获取高精度光学仪器表面图像,对表面图像进行灰度化处理、去噪处理与增强处理,以此为基础,应用SURF特征检测算法提取表面图像的特征点,通过FLANN算法匹配相邻图像的特征点,利用Delaunay三角化在表面图像区域形成三角网格,进一步计算表面图像特征点的位移与应变,从而实现高精度光学仪器表面动态变形的测量。实验数据显示:在横坐标为1μm时,应用提出方法获得的表面动态变形位移纵坐标为1.8μm,与实际位移基本保持一致,在实验组别为6时,应用提出方法获得的光学仪器表面动态变形应变数值为3μm,与实际应变数值保持一致,充分证实了提出方法具备较好的应用性能。

基于改进GWO-GRNN的管道焊缝三维重构测量

为提高双目相机不同位姿下焊缝的三维重构测量精度,提出一种基于立体视觉图像误差补偿的管道焊缝三维重构测量方法。采用改进灰狼算法(IGWO)优化广义回归神经网络(GRNN)补偿焊缝三维重构图像点的坐标误差。采用混沌映射、非线性收敛因子和最优记忆保存思想对GWO算法进行改进,通过8个标准测试函数进行仿真验证;利用优化后的GRNN模型对图像点坐标误差进行预测和补偿,计算三维坐标重构出焊缝点云,三维测量焊缝的焊宽、余高和长度。试验结果表明:该模型在双目相机不同的位姿状态下都能较准确地实现焊缝的三维重构,焊缝的三维测量相对误差在0.9%以内。

基于机器视觉的内啮合齿轮参数测量方法

为提高内啮合齿轮几何参数测量精度,提出了一种基于机器视觉的内啮合齿轮参数测量方法。首先,对工业相机进行标定,并对采集的图像进行滤波和二值化处理。然后,改进了传统Canny边缘检测算法的不足,提高了边缘检测的准确性。再次,采用重心法测量了齿轮中心,利用凸包法测量了齿顶圆半径,采用计算边缘图像像素坐标与齿轮中心的平均最近距离的方式算出了齿根圆直径,再对齿轮图像进行掩膜处理并以计算连通域的方式得到了齿轮的齿数,根据齿根圆、齿顶圆、模数和齿数间的关系得到齿轮的模数和分度圆直径。最后,测试所设计的视觉测量方法。实验表明,该方法能够准确测量内啮合齿轮几何参数。

河冰视频监测技术研究

本文分析了河冰视频监测的特点,围绕相机标定及投影转换、河冰特征识别、流冰速度解算等方面,系统介绍了河冰视频监测应用现状及关键技术,并提出应用过程中亟待解决的关键问题,为河冰视频监测技术研究提供了参考依据。

基于虚拟传感器的结构位移视觉测量误差抑制方法

温度、热浪和振动等环境因素给结构位移监测造成不可忽视的误差。虽然利用静止标志物降低视觉测量误差的原理可行,但是其适用范围却因背景标志物不易获得和控制而受到很大限制。利用视觉测量系统的现有条件,在近场布置主动光源作为误差检测虚拟传感器,摆脱对天然标志物的依赖。针对焦外成像产生的弥散圆等伴生问题,采用图像滤波、伽马变换、图像质量评估、图像二值化、边缘检测和目标检测等算法,形成一套面向弥散圆光斑的自适应图像增强算法;利用虚拟传感器的虚假位移,实现结构位移测量误差补偿,开展了仿真实验和室外结构位移视觉测量实验。研究结果表明:该方法可以显著降低结构位移的初始误差,残留误差满足测量精度要求。

基于亚像素边缘检测的板材直径测量研究

因像素级边缘检测精度低,导致板材的测量存在较大的误差。为此,提出了一种基于亚像素边缘检测的板材高精度测量方法。该方法假设物体边缘穿过的像素单元灰度值是不连续的,在各个边缘像素单元的矩形邻域内建立模型,准确计算出边缘的亚像素位置。通过张正友标定法校准相机,将图像坐标系与世界坐标系建立联系,计算出圆形PVC板材的直径。实验结果表明,在合成圆位置检测方面,半径的均方误差为0.034 1,检测精度较高。在实际图像测量方面,板材直径的均方误差为0.018 6,验证了该方法具有较高的稳定性和精度。

污染环境中织状复材蒙皮孔的视觉检测技术研究

碳纤维增强复合蒙皮一般通过连接孔与飞机骨架零件进行连接,连接孔的孔位对于保证连接强度至关重要。为了实现污染环境中织状复材蒙皮孔的高效在机检测,文章设计了一套机器视觉装置,提出了连接孔与激光光斑的图像算法,通过孔区域快速定位和临近边缘搜索实现了孔周边缘的精确提取,结合激光光束标定和光斑坐标值计算连接孔的孔位。在蒙皮试验件上开展了在机测孔实验,将文章的图像算法与常规图像算法进行对比。结果表明,文章的算法具有更好的适应性;孔位测量误差小于0.05 mm,检测效率达到每分钟12孔。

基于单目视觉实时测量桥式起重机负载摆角

针对目前基于视觉的桥式起重机负载摆角测量方法实时性较低,且没有考虑由运动模糊导致的测量精度下降问题,将一个对旋转和倾斜不敏感的球形标识物安装在负载上,设计一种计算灰度概率分布特征余弦相似度的方法,结合卡尔曼滤波器实时跟踪标识物。提出一种基于背景灰度值的局部自适应分割算法,使模糊边缘变清晰。从感兴趣区域(region of interest,ROI)的中心向周围8个方向寻找灰度梯度最大的点作为模糊图像的边缘点,对这些点采用最小二乘法进行拟合确定标识物位置。通过几何关系模型计算负载摆角。实验结果表明,本文方法在图像模糊情况下的测量精度和实时性能更优。