大学物理 ›› 2021, Vol. 40 ›› Issue (5): 28-.doi: 10.16854 / j.cnki.1000-0712.200332

• 物理实验 • 上一篇    下一篇

基于机器视觉的干涉条纹检测

王 叶,谢 雷   

  1. 1. 上海大学 理学院 物理系,上海 200444; 2. 上海大学 通信与信息工程学院 通信工程系,上海 200444
  • 收稿日期:2020-07-28 修回日期:2020-10-03 出版日期:2021-05-20 发布日期:2021-05-17
  • 作者简介:王叶( 1963—) ,男,上海人,上海大学理学院物理系高级实验师,学士,主要从事光电子、光纤应用和智能测控技术研究工作.

A detection of interference fringes based on Robot Vision

WANG Ye¹,XIE Lei²   

  1. 1.Department of Physics,Shanghai University,Shanghai 200444,China; 2. Department of Communication Engineering,Shanghai University,Shanghai 200444,China
  • Received:2020-07-28 Revised:2020-10-03 Online:2021-05-20 Published:2021-05-17

摘要:

基于机器视觉系统 OpenMV,以迈克耳孙干涉仪干涉条纹为研究识别对象,根据光源相干性及干涉条纹动态特性,提出基于灰度采样统计的干涉条纹识别检测算法( 干涉条纹同心形态搜索算法) ,运用 MicroPython 或 Python 语言编制实时检测程序,运行结果表明仿真和真实干涉条纹都得到了可靠的检测,精度为 0.5个条纹,并且测量不确定度主要由算法制定的此条纹检测精度引起,证明了干涉条纹机器视觉检测方法和技术的有效性.基于机器视觉系统 OpenMV,以迈克耳孙干涉仪干涉条纹为研究识别对象,根据光源相干性及干涉条纹动态特性,提出基于灰度采样统计的干涉条纹识别检测算法( 干涉条纹同心形态搜索算法) ,运用 MicroPython 或 Python 语言编制实时检测程序,运行结果表明仿真和真实干涉条纹都得到了可靠的检测,精度为 0.5

个条纹,并且测量不确定度主要由算法制定的此条纹检测精度引起,证明了干涉条纹机器视觉检测方法和技术的有效性.

关键词: 干涉条纹, 机器视觉, 形态搜索, 识别, 特征模板

Abstract:

Based on OpenMV,the system of Robot Vision,taking the interference fringes of Michelson

interfer- ometer as the object of recognition and study,according to the coherence of light source

and the dynamic characteris- tics of interference fringes,the identification and detection

algorithm of interference fringes based on gray sampling statistics is established( CSSA-IF:

Concentric shape searching algorithm for interference fringes) ,the real-time de- tection

program with MicroPython or Python language is written. Its implementation shows that the reliable

detection to the simulation or real interference fringes with the accuracy of 0.5,and the

uncertainty is mainly produced from this accuracy that lay down by the algorithm,meanwhile,the

availability of the methods and the techniques is proved.

Key words: interference fringe, Robot Vision, shape searching, recognition, feature template