Microstructural classification is typically done manually by human experts, which gives rise to uncertainties due to subjectivity and reduces the overall efficiency. A high-throughput characterization is proposed based on deep learning, rapid acquisition technology, and mathematical statistics for the recognition, segmentation, and quantification of microstructure in weathering steel. The segmentation results showed that this method was accurate and efficient, and the segmentation of inclusions and pearlite phase achieved accuracy of 89.95% and 90.86%, respectively. The time required for batch processing by MIPAR software involving thresholding segmentation, morphological processing, and small area deletion was 1.05 s for a single image. By comparison, our system required only 0.102 s, which is ten times faster than the commercial software. The quantification results were extracted from large volumes of sequential image data (150 mm², 62,216 images, 1024 × 1024 pixels), which ensure comprehensive statistics. Microstructure information, such as three-dimensional density distribution and the frequency of the minimum spatial distance of inclusions on the sample surface of 150 mm², were quantified by extracting the coordinates and sizes of individual features. A refined characterization method for two-dimensional structures and spatial information that is unattainable when performing manually or with software is provided. That will be useful for understanding properties or behaviors of weathering steel, and reducing the resort to physical testing.

微观结构分类通常由人类专家手动完成，这会由于主观性而产生不确定性并降低整体效率。提出了一种基于深度学习、快速采集技术和数理统计的高通量表征方法，用于耐候钢微观结构的识别、分割和量化。分割结果表明，该方法准确高效，夹杂物和珠光体相的分割准确率分别达到89.95%和90.86%。MIPAR 软件对单个图像进行阈值分割、形态处理和小区域删除所需的批处理时间为 1.05 s。相比之下，我们的系统只需要 0.102 秒，比商业软件快十倍。^2，62216张图片，1024×1024像素），保证全面统计。通过提取个体特征的坐标和尺寸，量化微观结构信息，例如三维密度分布和150 mm ^{2样品表面夹杂物最小空间距离的频率。}提供了一种在手动或使用软件时无法实现的二维结构和空间信息的精细表征方法。这将有助于了解耐候钢的特性或行为，并减少对物理测试的使用。