计算机与数字工程2024，Vol.52Issue(12)：3697-3702,6.DOI:10.3969/j.issn.1672-9722.2024.12.038
基于深度学习的纯铁晶粒显微图像分割方法
Microscopic Image Segmentation of Pure Iron Grains Based on Deep Learning
摘要
Abstract
Metallographic analysis is an important means of 3D construction of grain structure.To realize the calculation,mea-surement and visualization of the 3D spatial morphology of grains accurately,efficiently and quickly,it is necessary to extract the grain boundaries in the metallographic structure accurately.Traditional machine learning and deep learning models are extremely susceptible to noise interference such as grain boundary blurring,disappearance,and scratches during the extraction process,re-sulting in the inability to accurately extract grain boundaries.This paper proposes a convolutional neural network model GAU-Net that fused U-net and Conv Gate Recurrent Unit(convGRU).It aimes to solve the problem that the traditional convolutional neural network model cannot obtain the spatial trajectory information of the related pictures.The GAU-Net model ensures the input of the original image and correlates the high-level features of the temporal and spatial domains of the previous image by dual image map-ping.It uses a feedback mechanism that imitates human brain thinking.When the second round of network extracted features,the high-level features of the first round would be fused,and different types of feature fusion would be performed based on the size and dimension of the feature map.Model lightweighting is achieved while avoiding image feature loss.The findings show that,for the pure iron grain slice dataset,compared with other classical model algorithms,the method in the paper can accurately segment the grain boundaries in the complex environment.关键词
晶界提取/双模态映射/时空特征/多维度特征融合/模型轻量化Key words
grader extraction/dual mode picture mapping/spatial and space characteristics/multi-dimensional feature fu-sion/model lightweight分类
天文与地球科学引用本文复制引用
卜树川,程科..基于深度学习的纯铁晶粒显微图像分割方法[J].计算机与数字工程,2024,52(12):3697-3702,6.基金项目
国家自然科学基金项目(编号:61976241) （编号:61976241）
镇江市国际合作计划项目(编号:GJ2021008)资助. （编号:GJ2021008）
