Zhao Jianhong, Yang Huamin, Sui Yi
School of Computer Science and Technology, Changchun University of Science and Technology, Jilin, 130000, China.
State Key Laboratory of Baiyunobo Rare Earth Resource Researches and Comprehensive Utilization, Baotou Research Institute of Rare Earths, Baotou, 014030, China.
Sci Rep. 2025 Jan 11;15(1):1722. doi: 10.1038/s41598-024-85061-0.
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.
本研究介绍了一种基于深度学习的自动评估方法，用于通过扫描电子显微镜（SEM）分析钢的微观结构，旨在解决研究人员手动标记和主观评估的局限性。通过利用先进的计算机视觉算法，特别是一种适用于长期树枝状凝固的名为唐锐检测（TRD）的合适模型，该方法实现了对微观结构特征的高效准确检测和量化。这种方法不仅增强了训练过程，还简化了损失函数设计，最终对钢铁材料的表面改性进行了恰当评估。结果证明了该方法在材料科学中自动化和提高微观结构分析可靠性方面的潜力。
