## 3 Experiments and Results
### Performance Comparison of the REI Module with Different Occupancy Ratios  
To determine the impact of the scaling relationship between regular convolution and the REI module on the performance of the model, we designed a 12-layer deep coding and decoding network in which the coding structure consisted of convolutions of size three with a step size of one, REI modules, and max-pooling. The decoder was consistent with the framework in Section 2.4. We used it as the baseline model. We divided the baseline model according to the scale and configured it from deep to shallow layers. The proportion of the REI module to the baseline model was set to 0, 1/6, 1/3, 1/2, 2/3, 5/6, and 1. The relationship between the proportions of the convolution layer and the REI module in this network was determined by testing the inference speed and calculating the evaluation metrics. The training parameters were set as described in Section 3.2, and the training, validation, and test sets were the same as in Section 3.1. The results are shown in Table 1.  
To better evaluate the improvement in the network performance with the different module percentages, we used the PR curve to evaluate the impact on the model performance, and the result is shown in Figure 12. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ratio** & **MIOU** & **MPA** & **Recall** & **F1 Score** & **Inference Time (ms)** \\ \hline
0 & 0.7182 & 0.9758 & 0.7341 & 0.8146 & 3.45 \\
1/6 & 0.7149 & 0.9770 & 0.7320 & 0.8164 & 3.64 \\
1/3 & 0.7424 & 0.9850 & 0.7604 & 0.8276 & 4.17 \\
1/2 & 0.7354 & 0.9801 & 0.7589 & 0.8289 & 5.23 \\
2/3 & 0.7232 & 0.9724 & 0.7331 & 0.8143 & 6.45 \\
5/6 & 0.7312 & 0.9752 & 0.7381 & 0.8167 & 11.74 \\
1 & 0.7235 & 0.9757 & 0.7342 & 0.8182 & 22.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of experimental results for different ratios of REI modules in the baseline model. Figure 11: (**a**) Input-fused image of feature maps; (**b**) fused image of feature maps after \(1\times 3\) convolutions extraction; (**c**) fused image of feature maps after \(3\times 1\) convolutions extraction; (**d**) fused image of feature maps after \(3\times 3\) convolutions extraction; (**e**) fused image of feature maps after parallelization of \(1\times 3\), \(3\times 1\), and \(3\times 3\) convolutions extraction; and (**f**) fused image of feature maps extracted by the ACE module. The above pictures are represented using 32 bits. In summary, regardless of the comparison of performance indicators or using the PR curve to represent the performance of different proportions of convolution and REI modules, as the proportion of conventional convolutional layers increases, the speed of network inference is accelerated. This is due to the involution requiring more data reading-and-writing operations when performing inference compared to convolution, and the time lost to reading and writing is much larger than the time saved by the reduction in floating-point computations. Moreover, when involution is used for computation in shallow layers, the computational cost increases abruptly when large-resolution feature maps are unfolded, resulting in an obvious influence on the inference speed of the involution operator. In terms of the model capability, as the proportion of REI modules increases, the network performance increases first and then decreases. The model capability is optimal when the proportion is 1/3 and gradually decreases as the proportion becomes greater than 1/3. In view of this, we concluded that the attention patterns of distinct features are beneficial for the performance of the neural network at different levels. The shallow layers should be used to obtain as many features of the target as possible, while the deeper layers should reduce the channel redundancy, enhance the feature expression, and promote the classification capability of the network as a way to obtain higher model performance in terms of cost.