An effective on-line surface particles inspection instrument for large aperture optical element

被引：12

作者：

Ding W.-D. ^{[1
,2
]}

Zhang Z.-T. ^{[1
,2
]}

Zhang D.-P. ^{[1
,2
]}

Xu D. ^{[1
,2
]}

Lv H.-B. ^{[3
]}

Miao X.-X. ^{[3
]}

Zhou G.-R. ^{[3
]}

Liu H. ^{[3
]}

机构：

[1] Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing

[2] School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing

[3] Laser Fusion Research Center, China Academy of Engineering Physics, Mianyang

来源：

International Journal of Automation and Computing | 2017年 / 14卷 / 04期

基金：

中国国家自然科学基金;

关键词：

adaptive binarization; Dark-field imaging; image rectification; large aperture optical elements (LAOE); particle inspection;

D O I：

10.1007/s11633-017-1079-6

中图分类号：

学科分类号：

摘要：

Surface particles growing in large aperture optical element (LAOE) have significant impact on LAOE’s stable operation. It is a challenge for the online system to inspect the particles with long working distance, enough precision and high efficiency because of the system constraints. In this paper, an effective and portable inspection instrument is designed based on dark-field imaging principle. A Nikon lens and an industrial high definition (HD) camera are selected to construct the vision system to inspect particles of microns size spreading over hundreds of millimeters. Using two motors and other mechanical structure, the system can realize auto-focus and image rectification functions. The line light sources are installed on both sides of the LAOE in a sealed box while the vision system is portable and working outside the box. An adaptive binarization method is proposed to process the captured dark-field image. The distribution of particles on the LAOE’s surface is investigated. Because of the high resolution of the captured image, the SSE2 instructions optimization method is used to reduce the time cost of the algorithm. Experiments show that the instrument can inspect LAOE effectively and accurately. © 2017, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany.

引用

页码：420 / 431

页数：11

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