电子测量与仪器学报

柴瑞武,陈乐,何海森.基于改进亚像素算法的陶瓷天线PIN 针在线精密检测[J].电子测量与仪器学报,2023,37(11):170-177

基于改进亚像素算法的陶瓷天线PIN 针在线精密检测

Online precision detection of ceramic antenna PIN needle based on improved sub-pixel algorithm

DOI：

英文关键词:ceramic antenna PIN needle online detection sub-pixel algorithm image processing

基金项目:浙江省属高校基本科研业务费专项资金(2020YW29)项目资助

作者	单位
柴瑞武	1.中国计量大学机电工程学院
陈乐	1.中国计量大学机电工程学院
何海森	1.中国计量大学机电工程学院

Author	Institution
Chai Ruiwu	1.College of Mechanical and Electrical Engineering, China Jiliang University
Chen Le	1.College of Mechanical and Electrical Engineering, China Jiliang University
He Haisen	1.College of Mechanical and Electrical Engineering, China Jiliang University

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中文摘要:

陶瓷天线 PIN 针作为天线中重要的零部件之一,其尺寸偏差将直接关系到天线的产品质量。为了实现 PIN 针的在线精密检测,设计研发了一种 PIN 针在线检测装置,并提出了一种基于改进亚像素算法的 PIN 针尺寸检测方法。首先开始采集图像并进行像素当量标定,对图像进行畸变校正、获取 ROI 区域、图像预处理,然后利用基于改进 Sobel 算子及高斯峰值位置估算的亚像素边缘检测算法提取边缘点,利用最小二乘法将边缘点拟合成一对平行直线,并计算出线间像素宽度,根据像素当量换算得到被测 PIN 针在 ROI 区域处的直径尺寸。实验结果表明,该方法的测量平均相对误差小于 0. 25%,在保证±0. 005 mm 检测精度的同时,其平均耗时相对于传统基于高斯拟合的亚像素检测算法缩短了 64. 32%。

英文摘要:

As one of the important components in the antenna, the ceramic antenna PIN needle, its size deviation will directly affect the product quality of the antenna. In order to realize the fast and precise online detection of PIN needles, an online detection device for PIN needles was designed and developed, and a PIN needle size detection method based on an improved sub-pixel algorithm was proposed. First, start to collect images and perform pixel equivalent calibration, perform distortion correction on the image, obtain ROI area, and image preprocessing, then use the sub-pixel edge detection algorithm based on the improved Sobel operator and Gaussian peak position estimation to extract edge points, and use the least squares method, then the edge points are fitted into a pair of parallel straight lines, and the pixel width between the lines is calculated, and the diameter of the measured PIN needle at the ROI area is obtained by converting the pixel equivalent. The experimental results show that the average relative error of this method is less than 0. 25%. While ensuring ±0. 005 mm detection accuracy, its average time is reduced by 64. 32% compared to traditional sub-pixel detection algorithms based on Gaussian fitting.

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