Zhao, L.; Zhang, H.; Sun, X.; Xu, C.; Qin, X. Application of ResUNet-CBAM in Thin Section Image Segmentation of Rocks. Preprints2024, 2024102418. https://doi.org/10.20944/preprints202410.2418.v1
APA Style
Zhao, L., Zhang, H., Sun, X., Xu, C., & Qin, X. (2024). Application of ResUNet-CBAM in Thin Section Image Segmentation of Rocks. Preprints. https://doi.org/10.20944/preprints202410.2418.v1
Chicago/Turabian Style
Zhao, L., Chengwu Xu and Xudong Qin. 2024 "Application of ResUNet-CBAM in Thin Section Image Segmentation of Rocks" Preprints. https://doi.org/10.20944/preprints202410.2418.v1
Abstract
The application of deep learning convolutional neural networks (CNNs) for rock image characterization has become a prominent method in petroleum geology both nationally and internationally. Precise and automated segmentation of rock sheet images is essential for effective analytical studies. This research addresses the limitations of traditional image segmentation methods, which suffer from low accuracy and high costs in analyzing reservoir rock flakes. We propose an advanced model based on the U-Net architecture, incorporating both attention mechanisms and residual networks. The inclusion of residual modules enhances the network's depth, while the Convolutional Block Attention Module (CBAM) improves feature representation by adjusting the weighting of learned information. Extensive experimental results demonstrate that this approach delivers superior segmentation performance, marking a significant advancement in the field of reservoir rock image analysis.
Keywords
ResUNet-CBAM model; attention mechanism; image segmentation; deep learning; rock thin section image
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
