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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

8.7K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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Total Internal Reflection Fluorescence Microscopy01:05

Total Internal Reflection Fluorescence Microscopy

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Total internal reflection fluorescence microscopy or TIRF is an advanced microscopic technique used to visualize fluorophores in samples close to a solid surface with a higher refractive index, such as a glass coverslip. TIRF only allows fluorophores in proximity to the solid surface to be excited. When light from a medium with a lower refractive index (such as air) hits the glass coverslip at a critical angle, the light undergoes total internal reflection stead of passing through the glass.
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Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

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Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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相关实验视频

Updated: Jan 7, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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研究了一种改进的YOLOv8检测方法,用于光学元件表面缺陷的研究.

Bei Ma1, Jialong Zhao1, Shun Zhou1

  • 1School of Optoelectronic Engineering, Xi'an Technological University, Xi'an 710021, China.

Micromachines
|December 31, 2025
PubMed
概括

这项研究介绍了BACG-YOLOv8,这是一种用于检测光学元件表面缺陷的增强物体识别算法. 改进的模型在识别关键缺陷方面实现了更高的准确性和效率,这对高级应用至关重要.

关键词:
注意力机制注意力机制深度学习是一种深度学习.的光学元件的组成部分.表面缺陷 表面缺陷

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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
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Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments
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Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

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相关实验视频

Last Updated: Jan 7, 2026

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11:34

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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
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Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments
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科学领域:

  • 光学和材料科学 材料科学
  • 计算机视觉和人工智能的人工智能

背景情况:

  • 光学元件在航空航天,微电子和精度测量方面至关重要.
  • 表面缺陷降低了系统性能,需要先进的检测方法.
  • 目前的缺陷检测方法在分辨率,精度和效率方面面临挑战.

研究的目的:

  • 开发一种高分辨率,高精度和高效的光学表面缺陷检测算法.
  • 为了提高光学元件缺陷分析对象识别模型的性能.
  • 在复杂的检测场景中解决现有方法的局限性.

主要方法:

  • 提出了一个改进的YOLOv8对象识别算法.
  • BRA的注意力机制被整合到YOLOv8的骨干中,以增强多级特征处理.
  • 语境指南FPN模块取代了原来的功能融合模块,用于自适应的多尺度功能集成.
  • 该算法在高质量的显微镜暗场图像数据集上进行了训练和评估.

主要成果:

  • 增强的BACG-YOLOv8在光学元件缺陷检测方面表现出色.
  • 优化的网络准确地提取了缺陷细节,包括精细的边缘特征.
  • 该算法有效地抑制了噪声干扰,减少了检测错误.
  • 与基线模型相比,实现了更好的缺陷提取精度.

结论:

  • BACG-YOLOv8算法在光学表面缺陷检测方面取得了重大进展.
  • 整合BRA注意力和上下文指南FPN提高了复杂场景中的适应性和准确性.
  • 这种方法为确保光学元件的质量和性能提供了可靠的解决方案.