An end-to-end laser-induced damage change detection approach for optical elements via siamese network and multi-layer perceptrons
文献类型:期刊论文
作者 | Kou, Jingwei4,5; Zhan, Tao3; Wang, Li4; Xie, Yu2; Zhang, Yihui4; Zhou, Deyun5; Gong, Maoguo1 |
刊名 | Optics Express |
出版日期 | 2022-06-20 |
卷号 | 30期号:13页码:24084-24102 |
ISSN号 | 10944087 |
DOI | 10.1364/OE.460417 |
产权排序 | 1 |
英文摘要 | With the presence of complex background noise, parasitic light, and dust attachment, it is still a challenging issue to perform high-precision laser-induced damage change detection of optical elements in the captured optical images. For resolving this problem, this paper presents an end-to-end damage change detection model based on siamese network and multi-layer perceptrons (SiamMLP). Firstly, representative features of bi-temporal damage images are efficiently extracted by the cascaded multi-layer perceptron modules in the siamese network. After that, the extracted features are concatenated and then classified into changed and unchanged classes. Due to its concise architecture and strong feature representation ability, the proposed method obtains excellent damage change detection results efficiently and effectively. To address the unbalanced distribution of hard and easy samples, a novel metric called hard metric is introduced in this paper for quantitatively evaluating the classification difficulty degree of the samples. The hard metric assigns a classification difficulty for each individual sample to precisely adjust the loss assigned to the sample. In the training stage, a novel hard loss is presented to train the proposed model. Cooperating with the hard metric, the hard loss can up-weight the loss of hard samples and down-weight the loss of easy samples, which results in a more powerful online hard sample mining ability of the proposed model. The experimental results on two real datasets validate the effectiveness and superiority of the proposed method. © 2022 Optica Publishing Group |
语种 | 英语 |
出版者 | Optica Publishing Group (formerly OSA) |
WOS记录号 | WOS:000813479600147 |
源URL | [http://ir.opt.ac.cn/handle/181661/96039] |
专题 | 西安光学精密机械研究所_先进光学仪器研究室 |
通讯作者 | Zhou, Deyun |
作者单位 | 1.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Electronic Engineering, Xidian University, Xi'an; 710071, China 2.School of Computer and Information Technology, Shanxi University, Taiyuan; 030006, China; 3.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Computer Science and Technology, Xidian University, Xi'an; 710071, China; 4.The Advanced Optical Instrument Research Department, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; 5.School of Electronics and Information, Northwestern Polytechnical University, Xi'an; 710072, China; |
推荐引用方式 GB/T 7714 | Kou, Jingwei,Zhan, Tao,Wang, Li,et al. An end-to-end laser-induced damage change detection approach for optical elements via siamese network and multi-layer perceptrons[J]. Optics Express,2022,30(13):24084-24102. |
APA | Kou, Jingwei.,Zhan, Tao.,Wang, Li.,Xie, Yu.,Zhang, Yihui.,...&Gong, Maoguo.(2022).An end-to-end laser-induced damage change detection approach for optical elements via siamese network and multi-layer perceptrons.Optics Express,30(13),24084-24102. |
MLA | Kou, Jingwei,et al."An end-to-end laser-induced damage change detection approach for optical elements via siamese network and multi-layer perceptrons".Optics Express 30.13(2022):24084-24102. |
入库方式: OAI收割
来源:西安光学精密机械研究所
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