Damage Online Inspection in Large-Aperture Final Optics

摘要：

　　Under the condition of inhomogeneous total internal reflection illumination,a novel approach based on machine learning is proposed to solve the problem of damage online inspection in large-aperture final optics.The damage online inspection mainly includes three problems: automatic classification of true and false laser-induced damage(LID),automatic classification of input and exit surface LID and size measurement of the LID.We first use the local area signal-to-noise ratio(LASNR)algorithm to segment all the candidate sites in the image,then use kernel-based extreme learning machine(K-ELM)to distinguish the true and false damage sites from the candidate sites,propose autoencoder-based extreme learning machine(A-ELM)to distinguish the input and exit surface damage sites from the true damage sites,and finally propose hierarchical kernel extreme learning machine(HK-ELM)to predict the damage size.The experimental results show that the method proposed in this paper has a better performance than traditional methods.The accuracy rate is 97.46%in the classification of true and false damage; the accuracy rate is 97.66%in the classification of input and exit surface damage; the mean relative error of the predicted size is within 10%.So the proposed method meets the technical requirements for the damage online inspection.

关键词： Machine learning Laser-induced damage Damage online inspection Classification Size measurement

作者: Guodong Liu Fupeng Wei Fengdong Chen Zhitao Peng Jun Tang

作者单位: Institute of Optical Measurement and Intellectualization,Harbin Institute of Technology,Nangang Dist Institute of Optical Measurement and Intellectualization,Harbin Institute of Technology,Nangang Dist Research Center of Laser Fusion,China Academy of Engineering Physics,Youxian District,Mianyang 62190

会议类型: 国际会议

会议名称: 中国模式识别与计算机视觉大会(PRCV2018)

会议地点: 广州

会议语种:英文

页码: 237-248

在线出版日期: 2018-11-23（万方平台首次上网日期，不代表论文的发表时间）