基于KNN算法的镜片后弯曲率半径引起的焦度测量误差补偿  

Compensation of power measurement error caused by lens back curvature radius based on KNN algorithm

作　　者：高璇 金涛[1] 何荣 GAO Xuan;JIN Tao;HE Rong(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《光学技术》2022年第6期690-695,共6页Optical Technique

基　　金：国家重点研发计划资质(2020YFB2007501)项目资助。

摘　　要：在传统焦度计测量中,由于被测镜片后表面曲率半径变化,使得镜片后顶点与光阑表面无法完全重合,造成焦度测量误差随镜片度数增大而增大的情况。针对此传统测量上无法克服的测量误差,提出了一种利用不同焦度镜片图像提取不同特征的方法,获得了不同焦度镜片的特征数据集,并将特征数据集利用机器学习中k-近邻聚类算法(KNN algorithm)进行分类。选取折射率为1.551的-20~+20m^(-1)每隔1m^(-1)的单焦点镜片,折射率为1.56与1.60的-15~+8m^(-1)每隔0.5m^(-1)的单焦点镜片进行镜片图像采集。实验结果表明,所提方法下分类的精准度、召回率与F1评分均为100%,三个模型均能正确识别其对应的所有测试镜片,克服此测量误差。In the traditional focimeter measurement,due to the change of the curvature radius of the rear surface of the lens to be measured,the rear vertex of the lens and the diaphragm surface cannot be completely coincident,resulting in the situation that the power measurement error increases with the increase of the lens power.Aiming at the insurmountable measurement error in traditional measurement,a method of extracting different features from lens images with different powers is proposed,and the feature data sets of lenses with different powers are obtained.k-Clustering algorithm(KNN algorithm) for classification.Select single focus lenses-20 ~+20 m^(-1) with a refractive index of 1.551 every 1 m^(-1),and-15~+8 m^(-1) with a refractive index of 1.56 and 1.60 every 0.5 m^(-1) for lens image acquisition.The experimental results show that the classification Precision,Recall rate and F1 score of this method are all 100%,the three models can correctly identify all the corresponding test lenses and overcome this measurement error.

关 键 词：焦度计 测量误差 KNN算法 

分 类 号：TH741[机械工程—光学工程] TP301.6[机械工程—仪器科学与技术]