Accurately classifying microscopic damage helps automate the repair and recycling of National Ignition Facility optics and informs the study of damage initiation and growth. This complex 12‐class problem previously required human experts to distinguish and label the various damage morphologies. Finding image analysis methods to extract and calculate distinguishing features would be time consuming and challenging, so we sought to automate this task by using convolutional neural networks (CNNs) pretrained on the ImageNet database to take advantage of its automated feature discovery and extraction. We compared three model architectures on this dataset and found the one with highest overall accuracy, 99.17%, was a novel hybrid architecture, one in which we removed the final decision‐making layer of the deep learner and replaced it with an ensemble of decision trees (EDT). This combines the power of feature extraction by CNNs with the decision‐making strength of EDT. The accuracy of the hybrid architecture over the deep learning alone is shown to be significantly improved. Furthermore, we applied this novel hybrid architecture to an entirely different dataset, one containing images of repaired damage sites, and improved on the previously published findings, also with a demonstrably significant increase in accuracy over using the deep learner alone.

中文翻译：

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混合深度学习体系结构，用于对国家点火装置激光光学器件的微观损伤进行分类

对微观损伤进行准确分类有助于自动化国家点火装置光学器件的修复和回收，并为损伤引发和增长的研究提供依据。这个复杂的12级问题以前需要人类专家来区分和标记各种损坏形态。寻找图像分析方法来提取和计算区别特征将是耗时且具有挑战性的，因此我们寻求通过使用在ImageNet数据库上预训练的卷积神经网络（CNN）来自动执行此任务，以利用其自动特征发现和提取的优势。我们在此数据集上比较了三种模型架构，发现整体精度最高的模型架构为99.17％，是一种新颖的混合架构，在其中我们删除了深度学习者的最终决策层，并用一组决策树（EDT）代替了它。这将CNN的特征提取功能与EDT的决策能力结合在一起。事实证明，仅深度学习，混合架构的准确性就得到了显着提高。此外，我们将此新颖的混合体系结构应用于一个完全不同的数据集，其中包含修复的损坏部位的图像，并且对以前发表的发现进行了改进，与单独使用深度学习器相比，其准确性也明显提高。