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An end-to-end laser-induced damage change detection approach for optical elements via siamese network and multi-layer perceptrons
Kou, Jingwei1,2; Zhan, Tao3; Wang, Li2; Xie, Yu4; Zhang, Yihui2; Zhou, Deyun1; Gong, Maoguo5
作者部门先进光学仪器研究室
2022-06-20
发表期刊Optics Express
ISSN10944087
卷号30期号:13页码:24084-24102
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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

DOI10.1364/OE.460417
收录类别SCI ; EI
语种英语
WOS记录号WOS:000813479600147
出版者Optica Publishing Group (formerly OSA)
EI入藏号20222512252426
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/96039
专题先进光学仪器研究室
通讯作者Zhou, Deyun
作者单位1.School of Electronics and Information, Northwestern Polytechnical University, Xi'an; 710072, China;
2.The Advanced Optical Instrument Research Department, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, 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.School of Computer and Information Technology, Shanxi University, Taiyuan; 030006, China;
5.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Electronic Engineering, Xidian University, Xi'an; 710071, 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.
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