首页 > 论文 > 光学学报 > 38卷 > 6期(pp:612006--1)

基于遗传算法的数字图像相关法在微位移测量中的应用

Application of Digital Image Correlation Method Based on Genetic Algorithm in Micro-Displacement Measurement

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

对遗传算法、粒子群算法、人工鱼群算法三种整像素搜索算法在微位移测量中的应用进行了对比研究。以相关系数大小衡量图像的匹配精度,选择归一化互相关函数作为相关系数及算法的目标函数;对目标函数进行迭代求解,得到了整像素的微位移;以模拟散斑图为研究对象,对三种算法的匹配精度、搜索速度、微位移测量结果进行了对比分析。结果表明,遗传算法在匹配精度、搜索速度及微位移测量精度上具有明显的优势,能满足数字图像相关法在微位移测量中的应用需求。

Abstract

Three integer pixel search algorithms of genetic algorithm, particle swarm optimization, and artificial fish swarm algorithm applied in the micro-displacement measurement are studied and compared. The matching precision of the images is evaluated by the size of the correlation coefficient and the normalized cross-correlation function is selected as the correlation coefficient and the objective function of algorithms. The objective function is iteratively solved to obtain the micro-displacement result of the integer pixels. The simulated speckle pattern is taken as the research object, and the matching precision, searching speed, and micro-displacement measurement results for these three algorithms are compared and analyzed. The results show that the genetic algorithm has obvious advantages in the matching precision, searching speed and micro-displacement measurement precision, which can meet the application requirements of the digital image correlation method in the micro-displacement measurement.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TN247

DOI:10.3788/aos201838.0612006

所属栏目:仪器,测量与计量

基金项目:教育部留学回国人员科研启动基金(2015KJS010003)、安徽高校省级自然科学研究重点项目(KJ2016A037)、安徽省振兴计划(J05201343)

收稿日期:2018-01-15

修改稿日期:2018-02-04

网络出版日期:--

作者单位    点击查看

葛朋祥:安徽大学电气工程与自动化学院, 安徽 合肥 230601
叶沛:安徽大学电气工程与自动化学院, 安徽 合肥 230601
李桂华:安徽大学电气工程与自动化学院, 安徽 合肥 230601

联系人作者:李桂华(guihuali1@163.com)

备注:葛朋祥(1989-),男,硕士研究生,主要从事检测技术与自动化装置方面的研究。E-mail: 408074262@qq.com

【1】Yamaguchi I. A laser-speckle strain gauge[J]. Journal of Physics E, 1981, 14(5): 1270-1273.

【2】Peters W H, Ranson W F. Digital imaging techniques in experimental stress analysis[J]. Optical Engineering, 1981, 21(3): 427-431.

【3】Pan B, Wu D F, Xie H M, et al. Spatial-gradient-based digital volume correlation technique for internal deformation measurement[J]. Acta Optica Sinica, 2011, 31(6): 0612005.
潘兵, 吴大方, 谢惠民, 等. 基于梯度的数字图像相关法测量物体内部变形[J]. 光学学报, 2011, 31(6): 0612005.

【4】Pan B, Xie H M, Wang Z Y, et al. Study on subset size selection in digital image correlation for speckle patterns[J]. Optics Express, 2008, 16(10): 7037-7048.

【5】Zhang H J, Li G H, Liu C, et al. Reliable initial guess based on SURF feature matching in digital image correlation[J]. Acta Optica Sinica, 2013, 33(11): 1112005.
张华俊, 李桂华, 刘程, 等. 基于SURF特征匹配的数字图像相关变形初值可靠估计[J]. 光学学报, 2013, 33(11): 1112005.

【6】Tong W. An evaluation of digital image correlation criteria for strain mapping application[J]. Strain, 2005, 41(4): 167-175.

【7】Pan B, Xie H M, Wang Z Y. Equivalence of digital image correlation criteria for pattern matching[J]. Applied Optics, 2010, 49(28): 5501-5509.

【8】Liang S T, Yang S F, Xue B. A new phase diversity wave-front error sensing method based on genetic algorithm[J]. Acta Optica Sinica, 2010, 30(4): 1015-1019.
梁士通, 杨速峰, 薛彬. 基于遗传算法的改进相位差波前误差传感技术研究[J]. 光学学报, 2010, 30(4): 1015-1019.

【9】Chen G L, Chen H W, Guo W Z, et al. An improved GA based on RDAC and its application[J]. Pattern Recognition and Artificial Intelligence, 2004, 17(2): 250-256.
陈国龙, 陈火旺, 郭文忠, 等. 基于随机错位算术交叉的遗传算法及其应用[J]. 模式识别与人工智能, 2004, 17(2): 250-256.

【10】Pang W Z, Li J F, Cao Z H. Application of enhanced adaptive genetic algorithm in TDOA based location[J]. Applied Science and Technology, 2005, 32(6): 1-3.
庞伟正, 李俊峰, 曹志华. 改进的自适应遗传算法在TDOA定位中的应用[J]. 应用技术, 2005, 32(6): 1-3.

【11】Guo T Y, Li N N, Liu Y. Optimization of camera internal parameters based on particle swarm algorithm[J]. Laser & Optoelectronics Progress, 2017, 54(11): 111504.
郭彤颖, 李宁宁, 刘雍. 基于粒子群算法的摄像机内参数优化方法[J]. 激光与光电子学进展, 2017, 54(11): 111504.

【12】Wang D D, Xu Y B, Chen Q Q, et al. Absolute displacement measurement with point-diffraction interferometer based on quick searching particle swarm optimization algorithm[J]. Acta Optica Sinica, 2016, 36(1): 0112001.
王道档, 徐杨波, 陈茜茜, 等. 基于快速搜索粒子群算法的点衍射干涉绝对位移测量方法[J]. 光学学报, 2016, 36(1): 0112001.

【13】Li X L, Shao Z J, Qian J X. An optimizing method based on autonomous animals: fish-swarm algorithm[J]. System Engineering Theory and Practice, 2002, 22(11): 32-38.
李晓磊, 邵之江, 钱积新. 一种基于动物自治体的寻优模式: 鱼群算法[J]. 系统工程理论与实践, 2002, 22(11): 32-38.

【14】Liu D L, Li L L. New improved artificial fish swarm algorithm[J]. Computer Science, 2017, 44(4): 281-287.
刘东林, 李乐乐. 一种新颖的改进人工鱼群算法[J]. 计算机科学, 2017, 44(4): 281-287.

【15】Zhou P, Goodson K E. Subpixel displacement and deformation gradient measurement using digital image/speckle correlation[J]. Optical Engineering, 2001, 40(8): 1613-1620.

引用该论文

Ge Pengxiang,Ye Pei,Li Guihua. Application of Digital Image Correlation Method Based on Genetic Algorithm in Micro-Displacement Measurement[J]. Acta Optica Sinica, 2018, 38(6): 0612006

葛朋祥,叶沛,李桂华. 基于遗传算法的数字图像相关法在微位移测量中的应用[J]. 光学学报, 2018, 38(6): 0612006

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF