Automatic classification of true and false laser-induced damage in large aperture optics

作者：Wei, Fupeng; Chen, Fengdong^*; Liu, Bingguo; Peng, Zhitao; Tang, Jun; Zhu, Qihua; Hu, Dongxia; Xiang, Yong; Liu, Nan; Sun, Zhihong; Liu, Guodong^*

来源：Optical Engineering, 2018, 57(5): 053112.

DOI：10.1117/1.OE.57.5.053112

• laser-induced damage
• finite-difference time-domain
• feature vector
• kernel-based extreme learning machine

An automatic classification method based on machine learning is proposed to distinguish between true and false laser-induced damage in large aperture optics. First, far-field light intensity distributions are calculated via numerical calculations based on both the finite-difference time-domain and the Fourier optical angle spectrum theory for Maxwell's equations. The feature vectors are presented to describe the possible damage sites, which include true and false damage sites. Finally, a kernel-based extreme learning machine is used for automatic recognition of the true sites and false sites. The method studied in this paper achieves good recognition of false damage, which includes a variety of types, especially attachment-type false damage, which has rarely been studied before.

• 出版日期2018-5
• 单位中国工程物理研究院激光聚变研究中心; 哈尔滨工业大学; 中国工程物理研究院