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<p>This study introduces a deep learning-based automatic evaluation method for analyzing the microstructure of steel with scanning electron microscopy (SEM), aiming to address the limitations of manual marking and subjective assessments by researchers. By leveraging advanced computer vision algorithms, specifically a suitable model for long-term dendritic solidifications named Tang Rui Detect (TRD), the method achieves efficient and accurate detection and quantification of microstructure features. This approach not only enhances the training process but also simplifies loss function design, ultimately leading to a proper evaluation of surface modifications in steel materials. The results demonstrate the method’s potential in automating and improving the reliability of microstructural analysis in materials science.</p>

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Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月11日

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  • Jianhong Zhao,
  • Huamin Yang,
  • Yi Sui

摘要

This study introduces a deep learning-based automatic evaluation method for analyzing the microstructure of steel with scanning electron microscopy (SEM), aiming to address the limitations of manual marking and subjective assessments by researchers. By leveraging advanced computer vision algorithms, specifically a suitable model for long-term dendritic solidifications named Tang Rui Detect (TRD), the method achieves efficient and accurate detection and quantification of microstructure features. This approach not only enhances the training process but also simplifies loss function design, ultimately leading to a proper evaluation of surface modifications in steel materials. The results demonstrate the method’s potential in automating and improving the reliability of microstructural analysis in materials science.