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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (10): 1929-1935    DOI: 10.3785/j.issn.1008-973X.2020.10.009
计算机技术     
变照度下的视觉测量系统误差建模
粟序明1(),方成刚1,2,*(),潘裕斌1,吴伟伟3,李亚萍4,朱浪1
1. 南京工业大学 机械与动力工程学院，江苏 南京 210000
2. 南京工业大学 江苏省工业装备数字制造及控制技术重点实验室，江苏 南京 210009
3. 扬州大学 机械工程学院，江苏 扬州 225000
4. 华东理工大学 化学与分子工程学院，上海 200237
Modeling error of visual measurement system under changing illuminance
Xu-ming SU1(),Cheng-gang FANG1,2,*(),Yu-bin PAN1,Wei-wei WU3,Ya-ping LI4,Lang ZHU1
1. School of Mechanical and Power Engineering, Nanjing University of Technology, Nanjing 210000, China
2. Jiangsu Key Laboratory of Industrial Equipment Digital Manufacturing and Control Technology, Nanjing University of Technology, Nanjing 210009, China
3. College of Mechanical Engineering, Yangzhou University, Yangzhou 225000, China
4. School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
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摘要：

针对二维视觉在线测量工件时，照度变化因素导致测量误差的问题，提出基于遗传算法优化的最小二乘支持向量机（GA-LSSVM），建立照度误差模型的方法. 分析视觉测量系统的误差来源，通过最小二乘法分析照度影响下的误差规律. 利用照度变化误差实验，获得照度和测量系统的误差数据，分别训练GA-LSSVM、支持向量机（SVM）以及BP神经网络，建立照度和测量系统误差模型，对系统测量误差进行预测. 结果表明：在变照度测量误差预测模型中，GA-LSSVM模型、SVM模型及BP神经网络模型的预测精度分别为94.90%、90.23%及80.60%. 这表明遗传算法优化的最小二乘支持向量机建立的变照度误差模型，在拟合和预测精度上优于传统的BP神经网络.
关键词： 视觉测量;  误差建模;  遗传算法优化的最小二乘支持向量机;  照度;  BP神经网络    
Abstract:

A modeling method of the illuminance error was proposed based on genetic algorithm (GA) to optimize the least squares support vector machine (LSSVM) aiming at the problem that illuminance changes led to the error of measurement for online measuring two-dimensional visual artifacts. Vision measurement system error sources were analyzed, and illuminance under the influence was analyzed by the least squares method of error patterns. The illuminance and the error of the measurement system data were obtained by using the illumination change error of experiment. The illuminance and measurement system error model was established by training the GA - LSSVM, support vector machine (SVM) and BP neural network respectively to forecast the system measurement error. Results show that the prediction accuracy of GA-LSSVM model, SVM model and BP neural network model is 94.90%, 90.23% and 80.60% respectively in the prediction model of variable illuminance measurement error. The error model of variable illuminance that is established by the least square support vector machine and optimized by genetic algorithm is superior to the traditional BP neural network in terms of fitting and prediction accuracy.
Key words: visual measurement    error modeling    genetic algorithm optimization least squares support vector machine    illuminance    BP neural network
收稿日期: 2019-08-28 出版日期: 2020-10-28
CLC:  TP 391  
基金资助: 国家自然科学基金资助项目（51635003）；江苏省科技成果转化专项资金资助项目（BA2017099）
通讯作者: 方成刚     E-mail: 983438713@qq.com;279119134@qq.com
作者简介: 粟序明（1994—），男，硕士生，从事机器视觉高精密测量及智能装备研究. orcid.org/0000-0003-3717-9940. E-mail： 983438713@qq.com
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引用本文:

粟序明,方成刚,潘裕斌,吴伟伟,李亚萍,朱浪. 变照度下的视觉测量系统误差建模[J]. 浙江大学学报(工学版), 2020, 54(10): 1929-1935.

Xu-ming SU,Cheng-gang FANG,Yu-bin PAN,Wei-wei WU,Ya-ping LI,Lang ZHU. Modeling error of visual measurement system under changing illuminance. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 1929-1935.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.10.009    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I10/1929

图 1  视觉测量系统误差影响因素来源
图 2  测量误差与3个位置照度总和的关系
图 3  遗传算法变照度误差补偿模型方法
图 4  遗传算法优化的最小二乘支持向量机变照度误差补偿模型方法
图 5  变照度误差检测实验平台
图 6  实验轴提取的边缘
图 7  原图上拟合的边缘
图 8  3个不同位置照度计照度数据
图 9  不同照度下的原始误差数据
图 10  遗传算法优化的最小二乘支持向量机变照度误差模型预测结果
图 11  支持向量机变照度误差模型预测结果
图 12  BP神经网络变照度误差模型预测结果
模型 $\varDelta _{\rm{max} }/{ { {\text{μm} }} }$ ${\gamma }_{\rm{max} }/{\text{μm} }$ $\bar\varDelta /{\text{μm} }$ $ \delta / \text{%} $
遗传算法优化的最小二乘支持向量机变照度误差预测模型 114.3 5.83 2.19 94.90
支持向量变照度误差预测模型 114.3 10.92 4.11 90.50
BP神经网络变照度误差
预测模型 		114.3 22.14 5.67 80.60
表 1  变照度误差预测模型精度对比
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