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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
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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%。 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.
作者 柴瑞武 陈乐 何海森 Chai Ruiwu;Chen Le;He Haisen(College of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou 310018,China)
出处 《电子测量与仪器学报》 CSCD 北大核心 2023年第11期170-177,共8页 Journal of Electronic Measurement and Instrumentation
基金 浙江省属高校基本科研业务费专项资金(2020YW29)项目资助
关键词 陶瓷天线PIN针 在线检测 亚像素算法 图像处理 ceramic antenna PIN needle online detection sub-pixel algorithm image processing
作者简介 柴瑞武,2021年于浙江科技学院获得学士学位,现为中国计量大学硕士研究生,主要研究方向为机器视觉与图像处理技术。E-mail:chairuiwu@163.com;通信作者:陈乐,1978年于浙江大学获得学士学位,1994年于浙江大学获得硕士学位,2006年于浙江大学获得博士学位,现为中国计量大学教授,主要研究方向为计量测试与远程校准、在线检测与控制系统等。E-mail:clcjlu@126.com
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