Modeling error of visual measurement system under changing illuminance

doi:10.3785/j.issn.1008-973X.2020.10.009

Journal of ZheJiang University (Engineering Science)

2020, Vol. 54

Issue (10): 1929-1935 DOI: 10.3785/j.issn.1008-973X.2020.10.009

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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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

Received: 28 August 2019 Published: 28 October 2020

CLC:

TP 391

Corresponding Authors: Cheng-gang FANG E-mail: 983438713@qq.com;279119134@qq.com

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	Xu-ming SU
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	Yu-bin PAN
	Wei-wei WU
	Ya-ping LI
	Lang ZHU

Cite this article:

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.

URL:

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

变照度下的视觉测量系统误差建模

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

Fig.1 Source of visual measurement system error

Fig.2 Relation between measurement error and sum of illuminance of three positions’ illuminometer

Fig.3 Genetic algorithm to compensate error model of changing illuminance

Fig.4 GA optimized LSSVM variable illuminance error compensation model

Fig.5 Experimental platform for error detection of changing illuminance

Fig.6 Experimental axis extracts edge

Fig.7 Edge of fit on original drawing

Fig.8 Illuminance data of three different position's illuminometer

Fig.9 Original under error data

Fig.10 GA-optimized least square support vector machine variable illuminance error model

Fig.11 Results of support vector machine variable illuminance error model

Fig.12 Prediction results of BP neural network variable illuminance error model

Tab.1 Comparison of prediction accuracy of variable illuminance error model


[1]	AHN S J, WARNECKE H J, KOTOWSKI R Systematic geometric image measurement errors of circular object targets: mathematical formulation and correction[J]. The Photogrammetric Record, 1999, 16 (93): 485- 502 doi: 10.1111/0031-868X.00138

[2]	CUI J W, TAN J B, ZHOU Y, et al Improvement of vision measurement accuracy using Zernike moment-based edge location error compensation model[J]. Journal of Physics: Conference Series, 2007, 48 (1): 1353

[3]	杨君, 张涛, 宋靖雁, 等星点质心亚像元定位的高精度误差补偿法[J]. 光学精密工程, 2010, 18 (4): 1002- 1010 YANG Jun, ZHANG Tao, SONG Jing-yan, et al High precision error compensation method for sub-pixel positioning of star point centroid[J]. Optics and Precision Engineering, 2010, 18 (4): 1002- 1010

[4]	YANG S H, NATARAJAN U, SEKAR M, et al Prediction of surface roughness in turning operations by computer vision using neural network trained by differential evolution algorithm[J]. International Journal of Advanced Manufacturing Technology, 2010, 51 (9-12): 965- 971 doi: 10.1007/s00170-010-2668-5

[5]	邾继贵, 邹剑, 林嘉睿, 等摄影测量图像处理的高精度误差补偿法[J]. 光学学报, 2012, 32 (9): 129- 136 ZHU Ji-gui, ZOU Jian, LIN Jia-rui, et al High-precision error compensation method for photogrammetric image processing[J]. Acta Optica Sinica, 2012, 32 (9): 129- 136

[6]	ZHU W D, MEI B, YAN G R, et al Measurement error analysis and accuracy enhancement of 2D vision system for robotic drilling[J]. Robotics and Computer-Integrated Manufacturing, 2014, 30 (2): 160- 171 doi: 10.1016/j.rcim.2013.09.014

[7]	WANG J M, GAO B, ZHANG X, et al. Error correction for high-precision measurement of cylindrical objects diameter based on machine vision [C]// Proceedings of 2015 IEEE 12th International Conference on Electronic Measurement and Instruments. Qingdao: IEEE, 2015: 1113-1117.

[8]	DEND H X, WANG F, ZHANG J, et al Vision measurement error analysis for nonlinear light refraction at high temperature[J]. Applied Optics, 2018, 57 (20): 5556- 5565 doi: 10.1364/AO.57.005556

[9]	LI K, YUAN F, DING Z L, et al. Vision measurement error compensation research of double-theodolite based on neural network approaching [C]// Control Conference on IEEE Control Systems. [S. l.]: IEEE, 2011: 2815-2820.

[10]	SHEN Y J, ZHANG X, CHENG W, et al Quasi-eccentricity error modeling and compensation in vision metrology[J]. Measurement Science and Technology, 2018, 29 (4): 045006

[11]	LIU W, MA X, LI X, et al A novel vision-based pose measurement method considering the refraction of light[J]. Sensors, 2018, 18 (12): 4348- 4363 doi: 10.3390/s18124348

[12]	KLOSOWSKI M, JAKUSZ J A high-efficient measurement system with optimization feature for prototype CMOS image sensors[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67 (10): 2363- 2372 doi: 10.1109/TIM.2018.2814118

[13]	杜文华, 郭小刚, 曾志强, 等自适应光强变化的动态边缘位置提取方法[J]. 包装工程, 2018, 39 (3): 162- 166 DU Wen-hua, GUO Xiao-gang, ZENG Zhi-qiang, et al Dynamic edge position extraction method for adaptive light intensity variation[J]. Packaging Engineering, 2018, 39 (3): 162- 166

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