基于机器视觉的高精度微纳光纤直径实时测量

针对传统微纳光纤直径测量方法操作复杂、重复性差且易于损伤光纤等问题，开发了一套基于机器视觉的微纳光纤直径测量系统。首先，对系统采集的图像进行预处理和二值化分割，其次，通过Canny边缘算子实现微纳光纤边缘初定位，最后，基于改进Zernike矩的亚像素检测方法精确定位了亚像素级边缘。此外还提出了结合Hough变换与最小二乘法的算法拟合亚像素级边缘点的方案，将系统微纳光纤直径测量精度提升至纳米级。实验测量结果表明，该系统可实现3.51%以内误差的自动化测量，运行时间为2.671 s，更适用于微纳光纤尺寸的高精度实时测量。

High-precision and real-time measurement of micro-nano fiber diameter based on machine vision

To solve the problems of complex operations, poor repeatability, and contact damage in traditional micro-nano fiber diameter measurement methods, a new measurement system based on machine vision was developed. Firstly, the image was preprocessed and binary segmented in the system, and then the edge of micro-nano fiber was initially located by the Canny edge operator. After that, a sub-pixel detection method based on improved Zernike moment was used to accurately locate the sub-pixel edge. Besides, our system also proposed an algorithm combining Hough transform and the least square method to fit sub-pixel edge points, which improved the measurement accuracy of micro-nano fiber diameters to the nanometer level. The measurement results of micro-nano fiber diameter show that the system can achieve high automation with a measurement error of less than 3.51%, and the running time is 2.671 s. It can be applied to the high-precision and real-time measurement of micro-nano fiber morphology.