An automated optical inspection (AOI) platform for three-dimensional (3D) defects detection on glass micro-optical components (GMOC)

doi:10.1016/j.optcom.2023.129736

	An automated optical inspection (AOI) platform for three-dimensional (3D) defects detection on glass micro-optical components (GMOC)
	Du, Yinchao 1,2,3,7; Chen, Jiangpeng 7; Zhou, Han 4; Yang, Xiaoling 4; Wang, Zhongqi 5; Zhang, Jie 4,6; Shi, Yuechun 1,2,3; Chen, Xiangfei 1,2,3; Zheng, Xuezhe 7
	2023-10-15
发表期刊	OPTICS COMMUNICATIONS
ISSN	0030-4018
卷号	545 页码:7
摘要	With the widespread deployment of wavelength division multiplexing (WDM), optical transceivers increasingly use many glass micro-optical components (GMOC). Visual inspection of these GMOCs is a critical manufacturing step to ensure quality and reliability. However, manual inspection is often labor-intensive and time-consuming due to the transparent nature of glass components and the small, randomly located defects in three dimensions. Although automated optical inspection (AOI) exists, it has not yet been able to provide the desired level of accuracy and efficiency. This paper reports the development of an AOI platform for 3D defect detection on GMOCs. The platform incorporates 3D video acquisition and a novel two-stage neural network machine-learning algorithm. It includes a robotic arm for moving parts in 3D, a camera with an illumination module for video acquisition, and a video streaming processing unit with a machine vision algorithm for real-time defect detection on a production line. The robotic arm enables multi-perspective video capture of a test sample without refocusing. The twostage machine learning network uses a modified YOLOv4 architecture with color channel separation (CCS) convolution, an image quality evaluation (IQE) module, and a frame fusion module to integrate the single frame detection results. This network can process multi-perspective video streams in real-time for defects detection in a coarse-to-fine manner. The AOI platform was trained with only 30 samples and achieved promising performances with a recall rate of 1, a detection accuracy of 97%, and an inspection time of 48 s per part.
关键词	Automated optical inspection Glass micro -optical components Defects detection 3D video acquisition Machine-learning algorithm
DOI	10.1016/j.optcom.2023.129736
收录类别	SCI
语种	英语
WOS研究方向	Optics
WOS类目	Optics
WOS记录号	WOS:001043988600001
出版者	ELSEVIER
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21330
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Du, Yinchao
作者单位	1.Nanjing Univ, Natl Lab Solid State Microstruct, Nanjing 210093, Peoples R China 2.Nanjing Univ, Collaborat Innovat Ctr Adv Microstruct, Nanjing 210093, Peoples R China 3.Nanjing Univ, Microwave Photon Technol Lab, Coll Engn & Appl Sci, Nanjing 210093, Peoples R China 4.CAS Intelligent Comp Technol, Suzhou 215000, Peoples R China 5.Beijing Inst Technol, Beijing 100081, Peoples R China 6.Chinese Acad Sci, Inst Comp technol, Beijing 100090, Peoples R China 7.Innolight Technol Res Inst ITRI, 8, Xiasheng Rd, Suzhou Ind Pk, Suzhou 215000, Peoples R China
推荐引用方式 GB/T 7714	Du, Yinchao,Chen, Jiangpeng,Zhou, Han,et al. An automated optical inspection (AOI) platform for three-dimensional (3D) defects detection on glass micro-optical components (GMOC)[J]. OPTICS COMMUNICATIONS,2023,545:7.
APA	Du, Yinchao.,Chen, Jiangpeng.,Zhou, Han.,Yang, Xiaoling.,Wang, Zhongqi.,...&Zheng, Xuezhe.(2023).An automated optical inspection (AOI) platform for three-dimensional (3D) defects detection on glass micro-optical components (GMOC).OPTICS COMMUNICATIONS,545,7.
MLA	Du, Yinchao,et al."An automated optical inspection (AOI) platform for three-dimensional (3D) defects detection on glass micro-optical components (GMOC)".OPTICS COMMUNICATIONS 545(2023):7.