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English(EN) Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation

扩散模型增强金属合金TEM缺陷检测

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的数据增强技术，使用掩码条件潜在扩散模型生成合成透射电子显微镜（TEM）图像。该方法旨在改进金属合金中缺陷的检测和分类，特别是在数据稀缺的情况下。通过合成具有自动标记缺陷掩码的逼真图像，该方法增强了深度学习模型的训练，在缺陷分析中显示出适度的性能提升。 AI

影响 增强了在数据稀缺环境下显微镜图像分析的深度学习能力，可能促进材料科学研究。

排序理由 该集群包含一篇学术论文，详细介绍了一种用于显微镜图像分析的新数据增强方法。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ni Li, Nuohao Liu, Ryan Jacobs, Ajay Annamareddy, Maciej P. Polak, Kevin Field, Izabela Szlufarska, Dane Morgan ·

    Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation

    arXiv:2606.02532v1 Announce Type: new Abstract: Analyzing microstructural defects in transmission electron microscopy (TEM) images, particularly in irradiated metal alloys, is often limited by the availability of high-quality, labeled data. To address this, we introduce a generat…

  2. arXiv cs.CV TIER_1 English(EN) · Dane Morgan ·

    Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation

    Analyzing microstructural defects in transmission electron microscopy (TEM) images, particularly in irradiated metal alloys, is often limited by the availability of high-quality, labeled data. To address this, we introduce a generative data augmentation approach using a mask-cond…

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