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

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.

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