In this paper, a method of intelligent identification and data smooth processing of flying flexible joint pivoting center based on machine vision is proposed. The geometric center of the two markers attached to the flying body is located on a straight line at a certain angle to the center-line of the measured pivoting body, then continuous image sampling is carried out by industrial camera when the marker swings with the pivoting body, then image data is transmitted through a data interface to an industrial computer, then the image processing module de-noises the image, removes the background and locates the markers to obtain the plane coordinates of the markers in the coordinate system of the test system, then smooth the obtained coordinates. That is, by Matlab software, the coordinates of the mark points detected based on machine vision are optimized to obtain the smooth curve by fitting of the parabola and arc. Then the coordinates of the points on the curve are used to optimize the coordinates of the marked points from measurement. Then the optimized coordinate values are substituted into the calculation module of pivoting center, so the average pivoting center of the sampling interval of two images is calculated according to the mathematical model to approch the instantaneous pivoting center during the motion of the pivoting body. Then the result processing module displays and records the curve of pivoting center shift directly and effectively. Finally, it is validated by simulation and experiments that the precision of pivoting center measured by such measuring system is approximately 0.5%.

中文翻译：

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飞行柔性接头枢轴中心的智能识别与测试数据的平滑处理

提出了一种基于机器视觉的飞行柔性关节枢轴中心的智能识别和数据平滑处理的方法。附着在飞行物体上的两个标记的几何中心位于一条直线上，与所测枢轴的中心线成一定角度，然后当标记随枢轴摆动时，由工业相机进行连续图像采样机身，然后通过数据接口将图像数据传输到工业计算机，然后图像处理模块对图像进行消噪，去除背景并定位标记，以在测试系统的坐标系中获得标记的平面坐标，然后平滑获得的坐标。也就是说，通过Matlab软件，优化基于机器视觉检测到的标记点的坐标，以通过抛物线和圆弧的拟合获得平滑曲线。然后，曲线上的点的坐标用于根据测量结果优化标记点的坐标。然后将优化后的坐标值代入枢轴中心计算模块，从而根据数学模型计算出两个图像的采样间隔的平均枢轴中心，以在枢轴体运动过程中逼近瞬时枢轴中心。然后，结果处理模块直接有效地显示并记录枢转中心偏移的曲线。最后，通过仿真和实验验证，该测量系统测得的枢轴中心精度约为0.5％。