# A Lithology Recognition Network Based on Attention and Feature Brownian Distance Covariance
## 3 Experimental Results and Analysis
### Model Performance Comparison and Analysis  
In the realm of lithology analysis, the demand for robust and efficient models is imperative to precisely differentiate various rock types. Presently, mainstream lithology analysis algorithms encompass the ResNet series, which addresses the challenge of gradient vanishing during deep neural network training by introducing residual blocks. ResNet18 within  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Resnet18**} & **Improved** & \multirow{2}{*}{**FBDC**} & **Training** & **Testing** \\  & & **CBAM** & & **ACC/\%** & **ACC/\%** \\ \hline
1 & \(\surd\) & & & 92.63 & 91.70 \\
2 & \(\surd\) & \(\surd\) & & 96.60 & 95.79 \\
**FC-Res** & \(\surd\) & \(\surd\) & \(\surd\) & **97.96** & **97.60** \\ \hline \hline \end{tabular}  
* Text in bold indicates optimal indicators.  
\end{table}
Table 6: Results of FBDC module ablation experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Strategy of** & **Feature Map** & **Training** & **Testing ACC/\%** \\  & **Upgrading** & **Size** & **ACC/\%** & **Precision ACC/\%** \\ \hline
1 & No CBAM added & — & 92.63 & 91.70 \\
2 & CBAM & \(1\times 1\times\text{C}/2\) & 95.37 & 95.12 \\
3 & \(1\times 1\times 3\) & \(1\times 1\times\text{3C}\) & 95.54 & 94.14 \\
**4** & \(\mathbf{1\times 1\times 4}\) & \(\mathbf{1\times 1\times 4\text{C}}\) & **96.60** & **95.79** \\
5 & \(1\times 1\times 6\) & \(1\times 1\times\text{6C}\) & 95.33 & 95.14 \\ \hline \hline \end{tabular}  
* Text in bold indicates optimal indicators.  
\end{table}
Table 5: Performance analysis of CBAM model under different dimension-increasing strategies.  
the ResNet series exhibits a shallower network structure, rendering it suitable for resource-limited environments. In contrast, ResNet50 features a deeper network structure [29], demanding higher computational resources. The EfficientNet-B0 algorithm stands out for its heightened computational efficiency [30]. SENet [31], akin to ResNet in its fundamental structure, introduces an attention mechanism to enhance the network's sensitivity to vital features. MobileNet-V3 [32], on the other hand, stands as a lightweight convolutional neural network tailored for mobile devices.  
Figure 6: Visualization results of lithology identification.  
To gauge the efficacy of the proposed FC-Res model in rockiness recognition, this paper compares its algorithm against the original algorithms ResNet18, ResNet50, EfficientNet-B0, SENet, and MobileNet-V3, utilizing the same dataset for training. A comprehensive evaluation of model performance was conducted, considering Parameters, ACC, P, R, and F1. The experimental results are detailed in Table 7.  
As indicated in Table 7, ResNet50 and SENet exhibit larger parameter quantities, whereas the FC-Res model has a parameter count of 135.15M, which is merely 49.8% and 43.8% of that of ResNet50 and SENet, respectively. Despite this reduction, the FC-Res model demonstrates an improvement in accuracy by 4.03% and 4.97% compared to ResNet50 and SENet, respectively. Conversely, ResNet18, EfficientNet-B0, and MobileNet-V3 models possess fewer parameters but exhibit lower classification accuracy. Notably, the FC-Res model outperforms EfficientNet-B0 and MobileNet-V3 models, showcasing an accuracy improvement of 8.44% and 8.37%, respectively.