An Improved UNet++ Based Algorithm for Throat Segmentation in Cast Slice Images

YUE Feng-yun; HE Xiao-hai; TENG Qi-zhi; GONG Jian

doi:10.3969/j.issn.2096-6091.2023.23.003

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An Improved UNet++ Based Algorithm for Throat Segmentation in Cast Slice Images

RESEARCH PAPER | 更新时间：2025-03-26

- An Improved UNet++ Based Algorithm for Throat Segmentation in Cast Slice Images
- New Generation of Information Technology Vol. 6, Issue 23, Pages: 15-22(2023)
- 作者机构：
  
  1.四川大学电子信息学院，四川成都 610065
  2.成都西图科技有限公司，四川成都 610065
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2023.23.003
  CLC： TP391.41
- Accepted：2024-04-10，
  
  Published：15 December 2023
- 稿件说明：
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岳枫云, 何小海, 滕奇志, 等. 基于改进UNet++的铸体薄片图像喉道分割算法[J]. 新一代信息技术, 2023, 6(23): 15-22

YUE Feng-yun, HE Xiao-hai, TENG Qi-zhi, et al. An Improved UNet++ Based Algorithm for Throat Segmentation in Cast Slice Images[J]. New Generation of Information Technology, 2023, 6(23): 15-22
岳枫云, 何小海, 滕奇志, 等. 基于改进UNet++的铸体薄片图像喉道分割算法[J]. 新一代信息技术, 2023, 6(23): 15-22 DOI： 10.3969/j.issn.2096-6091.2023.23.003.

YUE Feng-yun, HE Xiao-hai, TENG Qi-zhi, et al. An Improved UNet++ Based Algorithm for Throat Segmentation in Cast Slice Images[J]. New Generation of Information Technology, 2023, 6(23): 15-22 DOI： 10.3969/j.issn.2096-6091.2023.23.003.

摘要

分析岩石中的喉道特征对岩石储层质量的衡量有着重要意义。传统的岩石铸体薄片图像喉道分割方法在提取喉道时主要依赖于孔隙的二值化图像，当分析复杂的孔隙结构时，这些方法容易使提取的喉道出现过分割和分割精度不高的问题，从而导致算法泛化性能差。针对上述问题，本文提出了一种基于深度学习的喉道分割算法。该算法采用UNet++作为特征提取的主干网络，在下采样过程中结合CBAM（Convolutional Block Attention Module）注意力模块以增强模型捕获不同维度特征之间相关性的能力，减小跳跃连接中的语义差异，从而提高喉道特征提取的准确性。本文算法与其他现有喉道分割算法分别就正确分割率CR（Correctness Rate）、欠分割率UR（Under-segmentation Rate）、过分割OR（Over-segmentation Rate）和MIoU（Mean Intersection over Union）四项指标进行了实验对比分析，结果表明本文方法能够有效抑制喉道过分割，在分割喉道时具有更高的准确度。

Abstract

Analyzing the characteristics of channels in rocks is important for assessing reservoir quality. Traditional methods for segmenting channels in rock thin section images mainly rely on binary images of pores for channel extraction. However

these methods tend to produce over-segmentation and low segmentation accuracy when analyzing complex pore structures

leading to poor generalization performance of the algorithm. To address these issues

this paper proposes a deep learning-based channel segmentation algorithm. The algorithm uses UNet++ as the backbone network for feature extraction. It incorporates CBAM (Convolutional Block Attention Module) attention modules during downsampling to enhance the model's ability to capture correlations between features of different dimensions and reduce semantic differences in skip connections. This improves the accuracy of channel feature extraction. Experimental comparisons of the proposed algorithm and other existing channel segmentation algorithms are conducted based on four metrics: CR (Correctness Rate)

UR (Under-segmentation Rate)

OR (Over-segmentation Rate)

and MIoU (Mean Intersection over Union). The results show that the proposed method can effectively suppress channel over-segmentation and achieve higher accuracy in channel segmentation.

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references

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