An automated optical inspection (AOI) platform for three-dimensional (3D) defects detection on glass micro-optical components (GMOC)
文献类型：期刊论文
| 作者 | Du, Yinchao4,5,6,7; Chen, Jiangpeng4; Zhou, Han2; Yang, Xiaoling2; Wang, Zhongqi3; Zhang, Jie1,2; Shi, Yuechun5,6,7; Chen, Xiangfei5,6,7; Zheng, Xuezhe4 |
| 刊名 | OPTICS COMMUNICATIONS
 |
| 出版日期 | 2023-10-15 |
| 卷号 | 545页码:7 |
| ISSN号 | 0030-4018 |
| 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 |
| 英文摘要 | 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. |
| WOS研究方向 | Optics |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:001043988600001 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21329]  |
| 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.Chinese Acad Sci, Inst Comp technol, Beijing 100090, Peoples R China 2.CAS Intelligent Comp Technol, Suzhou 215000, Peoples R China 3.Beijing Inst Technol, Beijing 100081, Peoples R China 4.Innolight Technol Res Inst ITRI, 8, Xiasheng Rd, Suzhou Ind Pk, Suzhou 215000, Peoples R China 5.Nanjing Univ, Natl Lab Solid State Microstruct, Nanjing 210093, Peoples R China 6.Nanjing Univ, Collaborat Innovat Ctr Adv Microstruct, Nanjing 210093, Peoples R China 7.Nanjing Univ, Microwave Photon Technol Lab, Coll Engn & Appl Sci, Nanjing 210093, 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. |
入库方式： OAI收割
来源：计算技术研究所
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