# Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism
## 4 Experiments
### Performance Comparison  
To validate the effectiveness of the CNN-SA network, a multi-source and multi-modal data fusion network based on the self-attention mechanism and various architecture-related networks was compared with the CNN-SA. The comparison models included CNN, CNN-CBAM [19], and CNN-LSTM [20], all of which had parallel network structures. CNN served as a baseline for comparison. Figure 7 illustrates the overall RMSE and loss function curves for different networks on the test set. In Figure 7a, the RMSE of each network experiences an initial decrease, followed by stability, with CNN-SA exhibiting the best accuracy and stability. Figure 7b presents the loss function curves for all networks, where CNN-SA demonstrates the fastest convergence speed and the lowest optimal value, further confirming the effectiveness of the proposed network. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Layers** & **Network Type** & **Number of Neurons** & **Kernel Size** & **Stride** & **Activation Function** \\ \hline
1 & Convolutional Layer & 16 & (3, 3) & 1 & ReLu \\ \hline
2 & Max-Pooling Layer & - & (2, 2) & 2 & - \\ \hline
3 & Convolutional Layer & 32 & (3, 3) & 1 & ReLu \\ \hline
4 & Max-Pooling Layer & - & (2, 2) & 2 & - \\ \hline
5 & Convolutional Layer & 64 & (3, 3) & 1 & ReLu \\ \hline
6 & Max-Pooling Layer & - & (2, 2) & 2 & - \\ \hline
7 & Flatten Layer & - & - & - & - \\ \hline
8 & Linear Layer & 256 & (50,176, 256) & - & - \\ \hline
9 & Convolutional Layer & 64 & 1 & 1 & - \\ \hline
10 & Batch Normalization Layer & 64 & - & - & ReLu \\ \hline
11 & Convolutional Layer & 128 & 1 & 1 & - \\ \hline
12 & Batch Normalization Layer & 128 & - & - & ReLu \\ \hline
13 & Convolutional Layer & 256 & 1 & 1 & - \\ \hline
14 & Batch Normalization Layer & 256 & - & - & ReLu \\ \hline
15 & Self-Attention Layer & 256 & - & - & - \\ \hline
16 & Linear Layer & 1036 & - & - & - \\ \hline
17 & Convolutional Layer & 128 & 1 & 1 & - \\ \hline
18 & Batch Normalization Layer & 128 & - & - & ReLu \\ \hline
19 & Convolutional Layer & 64 & 1 & 1 & - \\ \hline
20 & Batch Normalization Layer & 64 & - & - & ReLu \\ \hline
21 & Convolutional Layer & 3 & 1 & 1 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network specific parameter configuration table. To further analyze the performance of each network, the differences in the fusion results for each network were statistically measured using the validation set samples and plotted as box plots. The box plot in Figure 8 shows the distribution of the difference in values for each network. These results clearly show the advantages of the CNN-SA network. With a more compact distribution of disparity values and a lower median, it can more accurately predict the point cloud data that match the labeled data, further validating the effectiveness and superiority of the CNN-SA model in the point cloud reconstruction task. To provide a more intuitive comparison of the performance of each network in point cloud reconstruction tasks, a sample instance from the validation set was selected and inputted into each network for fusion. As shown in Figure 9, the fusion results of each network are compared with the label data, and the deviation maps of the fusion results and label data were plotted. It can be observed that the CNN-CBAM network exhibits significant deviations and distortions in the data fusion task. The fusion results  
Figure 8: Performances of different fusion networks. Figure 7: Results. (**a**) Test RMSE, (**b**) test loss. of the CNN network were slightly better than those of the CNN-CBAM network, but still exhibited some errors. In comparison, the fusion results of the CNN-LSTM network were markedly improved, with smaller errors. Compared with other networks, CNN-SA demonstrated higher point cloud matching and shape consistency, with smaller errors and smoother edges. By observing the deviation maps, it can be concluded that the CNN-SA network outperforms the other networks in multi-source and multi-modal data fusion tasks.