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相关实验视频

Updated: May 12, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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在照射后检查中通过转移学习量化出位类型的缺陷.

Michael Wu1, Jeremy Sharapov1, Matthew Anderson2

  • 1Idaho National Laboratory, Idaho Falls, ID, USA.

Scientific reports
|May 7, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种自动化方法,用于使用计算机视觉模型量化辐射核材料中的位移缺陷. 与手动分析相比,这种方法提高了准确性和效率,改善了材料的表征.

关键词:
失调缺陷量化量化的失调缺陷量化机器学习是机器学习.照射后的检查 照射后的检查

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科学领域:

  • 材料科学 材料科学 材料科学
  • 核工程 核工程是指核工程.
  • 计算机视觉 计算机视觉

背景情况:

  • 对被辐射材料中脱位缺陷的定量分析对于核能应用至关重要.
  • 手动的缺陷识别是耗时的,主观的,容易产生不一致.

研究的目的:

  • 开发一种自动化,准确和高效的工具,用于识别和量化被辐射材料中的脱位类型缺陷.
  • 用计算机视觉模型来利用转移学习来进行缺陷细分.

主要方法:

  • 利用YOLOv11开源计算机视觉模型进行缺陷识别和细分.
  • 员工转移学习,使用最小的注释微图集进行培训.
  • 应用该模型传输电子显微镜图像的辐射合金与不同噪声水平.

主要成果:

  • YOLOv11模型成功地同时分割了排位线和循环,即使在高噪音的微镜中也是如此.
  • 该模型在训练套件中不包括的合金上证明了有效性.
  • 在各种辐射合金中实现了高精度的缺陷量化.

结论:

  • 开发的计算机视觉工具提供了一种高度有效和可靠的方法来定量失位缺陷.
  • 这种自动化方法在核工业材料表征中的传统手动分析上显著改进.
  • 该工具适用于实际的辐射后检查分析.