The laser-induced damage change detection for optical elements using siamese convolutional neural networks

Jingwei Kou, Tao Zhan, Deyun Zhou, Wei Wang, Zhengshang Da, Maoguo Gong

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Due to the fact that weak and fake laser-induced damages may occur in the surface of optical elements in high-energy laser facilities, it is still a challenging issue to effectively detect the real laser-induced damage changes of optical elements in optical images. Different from the traditional methods, in this paper, we put forward a similarity metric optimization driven supervised learning model to perform the laser-induced damage change detection task. In the proposed model, an end-to-end siamese convolutional neural network is designed and trained which can integrate the difference image generating and difference image analysis into a whole network. Thus, the damage changes can be highlighted by the pre-trained siamese network that classifies the central pixel between input multi-temporal image patches into changed and unchanged classes. To address the problem of unbalanced distribution between positive and negative samples, a modified average frequency balancing based weighted softmax loss is used to train the proposed network. Experiments conducted on two real datasets demonstrate the effectiveness and superiority of the proposed model.

Original languageEnglish
Article number106015
JournalApplied Soft Computing
Volume87
DOIs
StatePublished - Feb 2020

Keywords

  • Change detection
  • Laser-induced damage
  • Siamese convolutional neural network
  • Weighted softmax loss

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