Calibration and detection of compound eye model

WANG Ke-yi; ZHANG Hao; CAO Zhao-lou; GUO Fang; WU Qing-lin; YAN Pei-zheng

doi:10.3788/OPE.20101808.1807

您当前的位置：

首页 >

文章列表页 >

Calibration and detection of compound eye model

Article | 更新时间：2020-08-12

- Calibration and detection of compound eye model
- Optics and Precision Engineering Vol. 18, Issue 8, Pages: 1807-1813(2010)
- 作者机构：
  
  中国科学技术大学精密机械与精密仪器系,安徽合肥,230027
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20101808.1807
  CLC： TH703
- Received：19 November 2009，
  
  Revised：18 January 2010，
  
  Published Online：20 August 2010，
  
  Published：20 August 2010
- 稿件说明：
移动端阅览
王克逸, 张浩, 曹兆楼, 郭方, 吴青林, 闫佩正. 复眼位标器的标定与探测[J]. 光学精密工程, 2010,18(8): 1807-1813

WANG Ke-yi, ZHANG Hao, CAO Zhao-lou, GUO Fang, WU Qing-lin, YAN Pei-zheng. Calibration and detection of compound eye model[J]. 光学精密工程, 2010,18(8): 1807-1813
王克逸, 张浩, 曹兆楼, 郭方, 吴青林, 闫佩正. 复眼位标器的标定与探测[J]. 光学精密工程, 2010,18(8): 1807-1813 DOI： 10.3788/OPE.20101808.1807.

WANG Ke-yi, ZHANG Hao, CAO Zhao-lou, GUO Fang, WU Qing-lin, YAN Pei-zheng. Calibration and detection of compound eye model[J]. 光学精密工程, 2010,18(8): 1807-1813 DOI： 10.3788/OPE.20101808.1807.

摘要

介绍了一种新型大视场、高灵敏度的复眼位标器

用于探测低空飞行目标并快速获取目标物体的运动参数。叙述了复眼模型的结构和制作方法

并利用Zemax对子眼成像通道光线追迹考察其成像特性。介绍了复眼标定与探测方法。采用基于LM算法的神经网络训练

建立各个通道精确的物像对应关系。为了检验神经网络标定算法的效果

采用传统的基于二次多项式拟合的算法进行校正对比。仿真结果表明

神经网络算法可以提供更好的精度

从像点可以准确地预测主光线的方向角(10

-3

~10

-4

rad)

而且具有易于集成

方便快捷的特点。此外

进行了目标定位检测的仿真实验

计算了若干目标点的坐标

结果表明各个坐标相对误差均在3%以内

对于牺牲空间横向分辨率来提升视场角的复眼光学系统

该结果符合目标探测的要求。

Abstract

A new compound eye model with wider fields of view and higher agility was introduced to track the low-flying targets in a complex background. The struction and preparation of the compound eye model were described and its imaging channels were traced by Zamax to evaluated imaging characteristics.The calibration and detection of the compound eye were introduced

then LM neural network calibration algorithm was trained to build the relationship between object points and corresponding image points. This calibration algorithm provides an accurate direction angle prediction from their corresponding image points

and it is easy to integrate into the system. Preliminary experimental results for neural network calibration were presented and evaluated

which shows that the residual errors between actual and measured direction angles are around 10

-3

~10

-4

rad. In detection simulation experiments

several points were calculated and results show that the errors between actual and calculated coordinates of position are within 3%. This is a good result for the compound eye sensor that sacrifices the spatial resolution to improve the angle resolution.

关键词

Keywords

references

LAND M F, STAVENGA D G. HARDIE R C, et al.. Variations in the Structure and Design of Compound Eyes[M]. in Facets Vision, New York: Springer Verlag, 1989,90-111.

JUSTH E W, KRISHNAPRASAD P S. Steering laws for motion camouflage . Proceeding Royal Soc. A, 2006,462:3629-3643.

BITSAKORS K, FERMULLER C. Depth estimation using the compound eye of dipteran flies[J]. Biological Cybernetics, 2006,95:487-501.

TANIDA J, KUMAGAI T, YAMADA K,et al.. Thin observation module by bound optics (TOMBO): concept and experimental verification[J]. Applied Optics, 2001,40(11):1806-1813.

FRANCHESCHINI N, PICHON J, BLANES C. From insect vision to robot vision[J]. Phil. Trans. Of Royal Soc. of London,1992, 337(1281):283-294.

HOSHINO K, MURA F, MORII H, et al.. A small-sized panoramic scanning visual sensor inspired by the flys compound eye . Proc. 1998 IEEE International Conference on Robotics and Automation, 1998,1641-1646.

LICHTENSTEIGER L, EGGENBERGER P. Evolving the morphology of a compound eye on a robot . IEEE Proc. Third European Workshop on Advanced Mobile Robotics, 1999,129 - 134.

LEVOY M, NG R, ADAMS A, et al.. Light field microscopy[J]. ACM Transactions on Graphics, 2006,25:924-934.

WILBURN B, JOSHI N, VAISH V, et al.. High performance imaging using large camera arrays[J]. In ACM Transactions on Graphics, 2005,24(3):765-776.

ADELSON E H , WANG J Y. Single lens stereo with a plenoptic camera . IEEE Trans. PAMI, 1992,14:99-106.

HORNSEY R, THOMAS P, WONG W, et al.. Electronic compound-eye image sensor: construction and calibration[J]. SPIE, 2004,5301:13-24.

KRISHNASAMY R, THOMAS P, PEPIC S, et al.. Calibration techniques for object tracking using a compound eye image sensor[J]. SPIE, 2004,5611:42-52.

张红鑫,卢振武,李凤有,等. 重叠复眼光学模型的建立与分析[J]. 光子学报, 2007,36(6):1106-1109.

ZHANG H X, LU Z W, LI F Y,et al.. The building and analysis of the superposition compound Eyes optical model [J]. Acta Photonica Sinica, 2007,36(6):1106-1109. (in Chinese)

张红鑫,卢振武,刘华,等. 对重叠复眼进行简化模拟与分析的新方法[J]. 光学精密工程,2008,16(10):1847-1851.

ZHANG H X, LU Z W, LIU H,et al.. Novel method to simulate and analyze superposition compound eye [J]. Opt. Precision Eng., 2008,16(10):1847-1851. (in Chinese)

张红鑫, 卢振武, 李凤有,等. 曲面复眼成像系统的研究[J]. 光学精密工程,2006,14(3):346-350.

ZHANG H X, LU ZH W ,LI F Y, et al.. Study on curved compound eye imaging system[J]. Opt. Precision Eng., 2006,14(3):346-350. (in Chinese)

陶圣,曾理江. 结合空间侧抑制的仿生复眼模型 [J]. 光学技术,2007,33(3):337-340.

TAO S, ZENG L J. Compound eye model based on the spatial lateral inhibition network[J]. Optical Technique, 2007,33(3):337-340. (in Chinese)

侯亚丽,李铁. 基于LM优化算法的BP神经网络目标识别方法[J]. 探测与控制学报,2008,30(1):53-57.

HOU Y L ,LI T. Improvement of BP neural network by LM optimizing algorithm in target identification[J]. Journal of Detection & Control, 2008,30(1):53-57. (in Chinese)

赵弘,顾继俊,林立. 基于LM神经网络的钻杆在线漏磁缺陷识别[J]. 装备制造技术,2009(2):87-89.

ZHAO H, GU J J, LIN L. Drill and casing pipes on- line inspection system based on levenberg- marquardt neural network[J]. Journal of Equipment Manufacture Technology, 2009(2):87-89. (in Chinese)

ZHAN Z X , WANG K Y . Fabrication of aspherical liquid lens controlled by electrostatic force . Proc. 3rd International Symp. on Advanced Optical Manufac. and Testing Tech.: Advanced Optical Manufacturing Technologies, 2007:6722-6725.

Views

668

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Full-stokes photodetector based on neural networks

Detection of conductive multi-particles based on circular convolutional neural network

Partial optimal transport-based domain adaptation for hyperspectral image classification

Thermal error prediction for grinding machine spindle based on heat conduction and convolutional neural network

Stiffness prediction-neural network based error compensation for attitude adjustment platform

Related Author

WANG Shoutong

ZHANG Ran

CHU Jinkui

MENG Hailong

CAI Dehao

LUO Chen

ZHOU Yijun

JIA Lei

Related Institution

Ningbo Research Institute， Dalian University of Technology

Liaoning Provincial Key Laboratory of Micro Nanosystems， Dalian University of Technology

College of Mechanical Engineering， Southeast University

Wuxi Shangshi-finevision Technology Co.， Ltd

College of Computer Science and Technology， Jilin University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰