智能计算机与应用2025,Vol.15Issue(1) :1-9.DOI:10.20169/j.issn.2095-2163.24110508

基于改进DeeplabV3+的岩心图像裂缝提取算法

Improved DeeplabV3+algorithm for fracture extraction in core images

胡健健 何小海 龚剑 卿粼波 滕奇志
智能计算机与应用2025,Vol.15Issue(1) :1-9.DOI:10.20169/j.issn.2095-2163.24110508

基于改进DeeplabV3+的岩心图像裂缝提取算法

Improved DeeplabV3+algorithm for fracture extraction in core images

胡健健 1何小海 1龚剑 2卿粼波 1滕奇志1
扫码查看

作者信息

  • 1. 四川大学 电子信息学院,成都 610041
  • 2. 成都西图科技有限公司,成都 610065
  • 折叠

摘要

岩心裂缝对于油气勘探有着重要意义,是宝贵的地质研究资料,对岩心裂缝图像进行裂缝提取有助于地质专家进行后续的研究工作.岩心裂缝图像存在着裂缝细小、裂缝和背景区域像素值接近等问题,已有的图像分割算法对裂缝提取效果不佳.为了改善岩心裂缝的提取效果,本文提出基于改进DeeplabV3+的岩心图像裂缝提取算法.本文算法设计了新的解码器,对图像进行充分多尺度特征融合,增强了模型对裂缝边缘细节的表征能力;同时,引入条形池化模块(SPM),作为空洞空间金字塔池化模块(ASPP)中的池化层,从而有效减少了背景区域对裂缝目标提取的干扰.实验结果表明,提出的基于改进DeeplabV3+的岩心图像裂缝提取算法,相比于原始 DeeplabV3+网络,改进算法在 mIoU、mPA、F1-Score 上分别提升了1.88%、4.49%、3.02%.

Abstract

Core fractures are of great significance for oil and gas exploration and serve as valuable geological research data.Extracting fractures from core fracture images helps geological experts in subsequent research work.However,core fracture images face challenges such as tiny fractures and pixels in the fracture and background areas being close in value,leading to unsatisfactory results from existing image segmentation algorithms.To improve the extraction of core fractures,this paper proposes an algorithm based on an improved DeeplabV3+for fracture extraction from core images.The proposed algorithm designs a new decoder that fully performs multi-scale feature fusion on the image,enhancing the model's ability to capture detailed fracture edges.Additionally,a Strip Pooling Module(SPM)is introduced as the pooling layer in the Atrous Spatial Pyramid Pooling(ASPP),effectively reducing interference from the background areas during fracture target extraction.Experimental results demonstrate that the proposed Improved DeeplabV3+Algorithm for Fracture Extraction in Core Images shows good performance in extracting fractures from core fracture images,with enhancements of 1.88%,4.49%,and 3.02%in mIoU,mPA,and F1 Score,respectively,compared to the original DeeplabV3+network.

关键词

岩心裂缝图像/DeeplabV3+/多尺度特征融合/条形池化

Key words

core fracture images/DeeplabV3+/multi-scale feature fusion/strip pooling

引用本文复制引用

出版年

2025
智能计算机与应用
哈尔滨工业大学

智能计算机与应用

影响因子:0.357
ISSN:2095-2163
段落导航相关论文