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A deep learning method for nanoparticle size measurement in SEM images.

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This study introduces an automated method for nanoparticle size measurement using an improved U-Net model. The technique enhances accuracy for small or low-contrast particles, improving efficiency in nanoparticle analysis.

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Image Analysis

Background:

  • Accurate nanoparticle size distribution is crucial for material performance and applications.
  • Manual nanoparticle size measurement in SEM images is inefficient and prone to errors.
  • Existing automated methods struggle with small particles, low contrast, and scalebar calibration.

Purpose of the Study:

  • To develop an automated, accurate, and efficient method for nanoparticle size measurement from SEM images.
  • To overcome limitations of existing semantic segmentation models in nanoparticle analysis.
  • To enable precise pixel-to-physical size conversion through automatic scalebar recognition.

Main Methods:

  • An improved U-Net model incorporating attention mechanisms (CBAM) and residual networks (ResNet50) was developed.
  • An automatic scalebar recognition algorithm was integrated for accurate pixel-to-physical size conversion.
  • The model was trained and evaluated on SEM images for nanoparticle segmentation and size measurement.

Main Results:

  • The enhanced U-Net model achieved high performance with IoU of 87.79% and F1-score of 93.50% on the test set.
  • A strong Spearman correlation coefficient of 0.91 was observed between automated and manual size measurements.
  • The mean relative error for particle size measurement was low at 4.25%, demonstrating high accuracy and robustness.

Conclusions:

  • The proposed automated method significantly improves the accuracy and efficiency of nanoparticle size measurement.
  • The integration of attention mechanisms and automatic scalebar calibration addresses key challenges in nanoparticle analysis.
  • This reliable automated tool facilitates nanoparticle characterization for research and engineering applications.