基于机器视觉的零件特征尺寸提取算法  

Extraction Algorithm of Part Feature Sizes Based on Machine Vision

作　　者：杨仁民[1] 郑洲[1] 陈斌 张学昌[1] 张炜[1] YANG Ren-min ZHENG Zhou CHEN Bin ZHANG Xue-chang ZHANG Wei(Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China Ningbo Liu He Packing Co., Ltd., Ningbo 315100, China)

机构地区：[1]浙江大学宁波理工学院,宁波315100 [2]宁波六和包装有限公司,宁波315100

出　　处：《包装工程》2017年第9期151-156,共6页Packaging Engineering

基　　金：国家自然科学基金(51075362);宁波市鄞州区科技局区重大产业技术创新专项(2016G002)

摘　　要：目的复杂型面零件功能特征多样,结构尺寸呈现空间分布,传统的手工检测方法无法满足检测工作要求,为提升检测效率,提出一种基于机器视觉的非接触式测量方法。方法使用CCD相机采集图像信息,对图像进行分析处理,获得圆的亚像素边缘轮廓,再通过最小二乘法进行圆拟合求得圆的参数方程,最后利用几何距离公式求得像素距离。通过系统标定求出像素当量,由像素当量最终求得圆与圆之间的实际距离。结果最小二乘拟合圆亚像素边缘检测算法稳定,抗噪性能较好,算法的分辨率为0.001个像素。结论该方法可正确、方便、有效地对零件进行尺寸测量。The work aims to propose a non-contact measurement method based on machine vision, in order to improve the testing efficiency with respect to the situation that the traditional testing method cannot meet the testing requirements as the function features of complex surface part are diverse and the structure size takes on spatial distribution.The image data was collected with CCD camera to analyze and process the images, so as to obtain the sub pixel edge contour of a circle.The parameter equation of circle was obtained based on circle fitting by using the least square method.Lastly, the pixel distance could be calculated with the geometric distance formula.Pixel equivalent was got by system calibration and the actual distance between circle and circle could be obtained based on pixel equivalent.The testing algorithm of sub pixel edge of circle by the least square fitting was stable with good anti-noise performance and 0.001 pixel resolution.The proposed method is correct, convenient and effective when it is used to measure the part size.

关 键 词：像素当量 亚像素边缘轮廓 最小二乘法拟合 提取特征尺寸 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]