作者：陈登旭¹, 刘吉^1,2, 武锦辉², 王鹏¹, 黄晓慧¹

作者单位：1. 中北大学信息与通信工程学院, 山西 太原 030051;
2. 中北大学 电子测试技术重点实验室, 山西 太原 030051

关键词：光学测量;DIC;整像素;改进人工鱼群算法;准确率

摘要：

针对目前在数字图像相关（digital image correlation，DIC）整像素位移测量领域中，多种算法存在容易陷入局部最优，导致部分点存在测量误差的问题，该文选择基于群体智能优化的人工鱼群算法，利用该算法本身具有的全局搜索能力，能够快速跳出局部最优的特点来改善这个问题，同时该算法还具有简单、快速、并行性等优点。为进一步提高该算法的准确率和效率，采用混沌均匀初始化和自适应视野步长的方法对原有算法进行改进。最后通过实验得出，改进人工鱼群算法可以成功应用于整像素位移搜索中，并且与常用的粒子群算法相比准确率明显提高，且位移越大，这种优势越明显。所以改进人工鱼群算法可以作为一种新的算法测量材料在变形后的整像素位移。

DIC deformation analysis based on improved artificial fish swarm algorithm
CHEN Dengxu¹, LIU Ji^1,2, WU Jinhui², WANG Peng¹, HUANG Xiaohui¹
1. School of Information and Communication Engineering, North University of China, Taiyuan 030051, China;
2. Key Laboratory of Electronic Testing Technology, North University of China, Taiyuan 030051, China
Abstract: For the current digital image correlation (DIC) whole pixel displacement measurement field, there are many algorithms that easily fall into the local optimum, resulting in measurement errors at some points. Here, an artificial fish swarm algorithm based on swarm intelligence optimization is selected. Using the global search capability of the algorithm, it can quickly jump out of the local optimal characteristics to improve this problem. At the same time, the algorithm has the advantages of simplicity, speed, and parallelism. In order to further improve the accuracy and efficiency of the algorithm, the method of uniform initialization of chaos and the step size of adaptive field of view are used to improve the original algorithm. Finally, through experiments, it is concluded that the improved artificial fish swarm algorithm can be successfully applied to the whole pixel displacement search, and the accuracy is significantly improved compared with the commonly used particle swarm algorithm, and the larger the displacement, the more obvious this advantage. So the improved artificial fish swarm algorithm can be used as a new algorithm to measure the whole pixel displacement of the material after deformation.
Keywords: optical measurement;DIC;integer pixel;improved artificial fish swarm algorithm;accuracy
2020, 46(5):114-119  收稿日期: 2019-12-19;收到修改稿日期: 2020-01-28
基金项目: 国家自然科学基金（61603352）；装备预研领域基金（6140003030210）；山西省自然科学基金（201901D111159）；山西省归国留学基金（2019-68）
作者简介: 陈登旭(1994-),女,山西吕梁市人,硕士研究生,专业方向为光电检测技术与图像处理
参考文献
[1] PETERS W F, RANSON M A, SUTTON T C, et al. Application of digitial correlation methods to ridid body mechanics[J]. Opt.Eng, 1983, 22(6)
[2] YAMAGUCHI I. A laser-speckle strain gauge[J]. Journal of Physics E Scientific Instruments, 2000, 14(11): 1270-1273
[3] PETERS W H. Digital imaging technique in experimental stress analysis[J]. Optical Engineering, 1982, 21(3): 427-431
[4] CHU T C, RANSON W F, SUTTON M A. Applications of digital-image-correlation techniques to experimental mechanics[J]. Experimental Mechanics, 1985, 25(3): 232-244
[5] BRUCK H A, MCNEILL S R, SUTTON M A, et al. Digital image correlation using Newton-Raphson method of partial differential correction[J]. Experimental Mechanics, 1989, 29(3): 261-267
[6] 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
[7] 卢有麟, 周建中, 李英海, 等. 基于混沌搜索的自适应差分进化算法[J]. 计算机工程与应用, 2008, 44(10): 31-33
[8] 吴亮红, 王耀南, 袁小芳, 等. 自适应二次变异差分进化算法[J]. 控制与决策, 2006, 21(8): 898-909
[9] 马宪民, 刘妮. 自适应视野的人工鱼群算法求解最短路径问题[J]. 通信学报, 2014, 35(1): 1-6
[10] HU X T, ZHANG H Q, LI Z C, et al. A novel self-adaptation hybrid artificial fish-swam algorithm[J]. IFAC Proceedings, 2013, 46(5): 583-588
[11] 王明亮, 闵新力, 薛君志. 基于改进人工鱼群算法的WSN覆盖优化策略[J]. 微电子学与计算机, 2015, 32(6): 78-81
[12] ZHOU P, GOODSON K E. Subpixel displacement and deformation gradient measurement using digital image/speckle correlation (DISC)[J]. Optical Engineering, 2001, 40(8): 1613-1620