球面光学元件表面疵病视觉检测方法研究  被引量:4

Visual Detection Method of Spherical Optical Elements Surface Defects

在线阅读下载全文

作  者:胡泽波 郭忠达[1] 李宏 龚卓 金永红 HU Ze-bo;GUO Zhong-da;LI Hong;GONG Zhuo;JIN Yong-hong(School of Photoelectric Engineering,Xi’an Technologixal University,Xi’an 710021,China;Shanxi Key Laboratory of thin film technology and optical testing,Xi’an 710021,China;Xi’an University of Technology-Dongguan Yutong Optical Joint Laboratory,Xi’an 710021,China)

机构地区:[1]西安工业大学光电工程学院,陕西西安710021 [2]陕西省薄膜技术与光学检测重点实验室,陕西西安710021 [3]西安工业大学—东莞市宇瞳光学联合实验室,陕西西安710021

出  处:《光学与光电技术》2023年第1期21-27,共7页Optics & Optoelectronic Technology

基  金:陕西省科技计划项目(2019GY-081);西安工业大学—东莞市宇瞳光学联合实验室项目(H202105108)。

摘  要:球面光学元件由于其光学结构的影响,采用机器视觉的方法对其表面疵病进行检测时,无法将被测面都成像在一个像平面上并且在成像的过程中丢失了疵病的三维信息,造成了检测的误差。为了解决这些问题,提出了一种机器视觉与三维重构相结合的检测方法。首先,根据球面光学元件的特性设计了图像采集平台,以获得高质量的疵病图像。然后,通过图像处理算法对疵病图像进行预处理与疵病识别。最后,基于计算机视觉图像重构技术与球心投影技术,对疵病图像进行三维重构。实验表明,该方法提升了机器视觉对球面光学元件表面疵病检测的精度,可达99%,具有可行性和研究价值。Due to the influence of its optical structure,when using the method of machine vision to detect the surface defects of spherical optical components,it is impossible to image the measured surface on one image plane,and the threedimensional information of the defects is lost during the detection process.detection error.To solve these problems,a detection method combining machine vision and 3D reconstruction is proposed.First,an image acquisition platform is designed according to the characteristics of spherical optical elements to obtain high-quality defect images.Then,the defect image is preprocessed and identified by the image processing algorithm.Finally,based on the computer vision image reconstruction technology and the spherical center projection technology,the 3D reconstruction is carried out on the image of the defect.Experimental results show that this method improves the accuracy of machine vision detection of spherical optical components surface defects,up to 99%,which is feasible and valuable for research.

关 键 词:机器视觉 图像处理 疵病识别 图像重构技术 球心投影 

分 类 号:TN391.41[电子电信—物理电子学]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象