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深度学习方法用于TEM中的失调细分.

Assya Boughrara¹, Christine Viala¹, Laurent Dupuy²

¹Université de Toulouse, CNRS, CEMES, Toulouse, France.

Ultramicroscopy

|February 26, 2026

概括

此摘要是机器生成的。

深度学习模型,包括半监督学习,在传输电子显微镜 (TEM) 图像中显著改善了位移细分,接近专家性能. 这有助于更快地分析材料属性.

关键词:

深度学习是一种深度学习.失调细分失调细分失调细分监督和半监督的学习.

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科学领域:

材料科学材料科学材料科学
计算材料科学科学计算材料科学
数据科学数据科学数据科学

背景情况:

脱位动态极大地影响合金的机械性能.
使用传输电子显微镜 (TEM) 分析失位需要专业知识,并且耗时.

研究的目的:

开发深度学习方法,用于TEM图像中的自动化位移细分.
提高跨多种材料和成像条件的脱位分析的效率和准确性.

主要方法:

实现完全监督学习 (FSL) 和半监督学习 (SSL),使用带有边界类型损失的编码器解码器神经网络.
使用大型内部未标记数据集来丰富SSL中的功能描述.
探索通过模拟生成的物理接地合成图像的域调整.

主要成果:

与FSL相比,SSL方法显示了较好的评估指标,接近人类专家的性能.
直接将知识从合成图像转移到真实图像的成功有限.
合成图像证明在具有挑战性的成像场景中改善预测是有益的.

结论:

深度学习,特别是SSL,提供了一种强大的工具,用于自动化和改进TEM中的失调细分.
虽然合成数据在直接知识传输方面存在局限性,但它可以在特定具有挑战性的成像条件下提供帮助.
自动化脱位密度测量是有前途的未来应用.