基于改进Zernike矩的轴类零件尺寸测量方法

轴类零件尺寸视觉测量当前多采用像素级边缘检测算法，难以适应工业自动化高精度测量需求。为提高轴类零件轴径尺寸测量精度，本文提出一种基于改进Zernike矩的轴类零件尺寸测量方法。首先利用Canny边缘检测算法对轴图像进行粗定位，获取像素级边缘。其次，根据图像目标与背景间灰度差异，提出基于多阈值Otsu的Zernike矩最佳判定阈值获取方法，获取亚像素级边缘。最后，提出基于边缘点搜索的改进最小二乘法拟合轴图像边缘直线，获取轴径尺寸测量值。实验结果表明，以自行车后轴轴径尺寸测量为例，本文算法相比传统Zernike矩方法稳定性更高，与人工测量值相对误差在0.011%以内，测量精度满足工业零件尺寸测量中自行车轴直径6级公差精度要求。

Improved Zernike moment-based dimensional measurement method for shaft parts

Currently, pixel-level edge detection algorithms are mostly used in the dimension vision measurement for shaft parts, which can hardly be adapted to the high-precision measurement requirements of industrial automation. In order to improve the measurement accuracy of shaft diameter of shaft parts, a dimensional measurement method for shaft parts based on improved Zernike moment is proposed. Firstly, Canny edge detection algorithm is used for rough positioning in shaft images to obtain pixel-level edges. Then, in accordance with the grayscale difference between the target and the background information of the image , a method for obtaining the optimal decision threshold of Zernike moment based on the multi-threshold Otsu method is proposed to obtain sub-pixel edges. Finally, an improved least square method to fit the edge line of shaft images based on edge point search is proposed to obtain the measurements of the shaft diameter. The experimental results show that, taking the measurement of bicycle rear axle diameter as an example, the proposed algorithm has better stability than the traditional Zernike moment method, and the relative error with the manual measurement is less than 0.011%. The measurement accuracy of the proposed method can meet the accuracy requirement of 6-level of bicycle axle diameter in industrial parts dimension measurement.