磁铁矿显微图像的深度学习语义分割  

DEEP LEARNING SEMANTIC SEGMENTATION OFMAGNETITE MICROSCOPIC IMAGE

作　　者：梁秀满 姚珊珊 薛文强 牛福生 张晋霞 Liang Xiuman;Yao Shanshan;Xue Wenqiang;Niu Fusheng;Zhang Jinxia(College of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,Hebei,China;College of Mining Engineering,North China University of Science and Technology,Tangshan 063210,Hebei,China)

机构地区：[1]华北理工大学电气工程学院,河北唐山063210 [2]华北理工大学矿业工程学院,河北唐山063210

出　　处：《计算机应用与软件》2025年第3期196-201,283,共7页Computer Applications and Software

基　　金：国家自然科学基金项目(51874135);河北省自然科学基金项目(E2019209347);唐山市科技创新团队培养计划项目(19130207C)。

摘　　要：矿物分割是矿石鉴定的基础。由于现有的语义分割网络泛化性不强,因此提出一种多特征融合的U型语义分割网络MFF-Net。以交代残余结构磁铁矿显微图像为研究对象。采用语义分割网络将磁铁矿显微图像根据脉石颜色分割成深色区域和浅色区域并进行中值滤波;利用Canny边缘检测和固定阈值分割对深、浅色区域进行处理,将图像融合得到最终的分割图。对比实验结果表明,语义分割网络相比于其余的U型网络计算量得到极大的减少,并且只采用4幅带标签图片进行训练时仍能很好地分割出目标轮廓。Mineral segmentation is the basis of ore identification.Due to the weak generalization of the existing semantic segmentation network,a multi feature fusion U-shaped semantic segmentation network(MFF-Net)is proposed.The microscopic image of metasomatic residual magnetite was studied.The magnetite microscopic image was divided into dark and light regions according to the gangue color by semantic segmentation network,and the median filter was performed.Canny edge detection and fixed threshold segmentation were used to process the dark and light color regions,and the image was fused to obtain the final segmentation image.Through the experiments,compared with other U-shaped networks,the computation of semantic segmentation network is greatly reduced,and it can still segment the target contour well when only four labeled images are used for training.

关 键 词：矿物显微图像 语义分割 深度学习 特征融合 实时 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]