现代电子技术2026，Vol.49Issue(9)：79-86,8.DOI:10.16652/j.issn.1004-373x.2026.09.012
基于MEGNet的岩心孔洞图像分割算法
Core hole image segmentation algorithm based on MEGNet
摘要
Abstract
In view of the high similarity between foreground and background,large difference in hole morphology and blurred edge segmentation in core hole segmentation,this paper proposes an edge-guided segmentation model MEGNet based on Mamba.Firstly,the parallel PVM Block is designed as the backbone module to model the long-range dependence,which reduces the number of parameters and alleviates the missegmentation.Secondly,an edge generation(EG)module is constructed to generate edge features by fusing low-level details and high-level semantic features.Then,an edge-guided attention(EGA)module is proposed to optimize edge details by combining reverse features and multi-scale channel attention module(MS-CAM).Finally,the feature enhancement module(FEM)is introduced,and the multi-scale context information is captured by multi-expansion rate dilated convolution to enhance the expression of key features.Experiments show that the F1-score,intersection over union(IoU)and mean intersection over union(MIoU)of MEGNet on the core hole dataset reach 88.83%,79.92%and 89.73%,respectively.The proposed method has better segmentation effect and excellent performance in comparison with the mainstream semantic segmentation models.关键词
岩心孔洞/深度学习/Mamba/边缘特征/特征增强/注意力模块Key words
core hole/deep learning/Mamba/edge feature/feature enhancement/attention module分类
信息技术与安全科学引用本文复制引用
覃洪杰,沈疆海,张乐..基于MEGNet的岩心孔洞图像分割算法[J].现代电子技术,2026,49(9):79-86,8.基金项目
中国高校产学研创新基金(2021ALA01004) （2021ALA01004）
新疆自治区创新人才建设专项自然科学计划(2020D01A132) （2020D01A132）
