## 4 Experiments and Results
### Terrain Recognition Results  
To verify the performance of the proposed LA-MobileNet model, we compare it with several state-of-the-art classification methods, including VGG16, ResNet50, ShuffleNetV2, MobileNetV3, EfficientNet [37], InceptionV3 [38] and DenseNet. The experimental results are shown in Table 2. It can be seen that LA-MobileNet achieved better results. Compared with Resnet50, a model with a larger number of parameters, the indicators Accuracy, Recall, F1-score and Precision have increased by 0.7%, 1.35%, 1.34% and 0.87%, respectively. Compared with the lightweight model ShuffleNetV2, the indicators Accuracy, Recall, F1-score and Precision have increased by 2.16%, 4.78%, 4.33% and 2.05%, respectively.  
To verify the effectiveness of auxiliary loss and CA, respectively, we conducted experiments, and the results are shown in Table 3. Among them, MobileNetV3+Auxloss and MobileNetV3+CA introduce auxiliary loss and CA into the MobileNetV3 network, respectively, and LA-MobileNet simultaneously introduces auxiliary loss and CA into the MobileNetV3 network. It can be seen that after the introduction of auxiliary loss, Accuracy, Recall, F1-score and Precision increased by 1.80%, 5.05%, 3.63% and 1.98%, respectively, which verified that the auxiliary loss function can provide additional supervisory information during the model training process, help the network learn more complete feature information, and enhance its generalization ability, thereby improving the classification accuracy of the model. After fusing CA, Accuracy, Recall, F1-score and Precision increased by 2.33%, 3.93%, 3.87%, 2.96%, respectively, which verifies that CA can effectively capture the spatial position information in the feature map, improve the model's perception of spatial features, and thereby improve the accuracy of classification. When the above two improvement points are adopted at the same time, the performance of the network is further improved, achieving 96.09% Accuracy, 95.15% Recall, 95.47% F1-score and 96.02% Precision, which proves the effectiveness of the improvements. In addition, the number of parameters of LA-MobileNet is 3.26 M, the floating-point operation amount is 0.31 G, and the time required to predict a single image on our experimental platform is 24.85 ms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Recall** & **F1-Score** & **Precision** \\ \hline VGG16 [9] & 0.8844 & 0.8036 & 0.7834 & 0.7896 \\ Resnet50 [10] & 0.9539 & 0.9380 & 0.9413 & 0.9515 \\ ShuffleNetV2 [14] & 0.9393 & 0.9040 & 0.9114 & 0.9397 \\ MobileNetV3 [16] & 0.9335 & 0.8881 & 0.8941 & 0.9241 \\ EfficientNet [37] & 0.9189 & 0.8733 & 0.8790 & 0.9407 \\ InceptionV3 [38] & 0.9317 & **0.9519** & 0.9410 & 0.9395 \\ DenseNet [11] & **0.9644** & 0.9380 & 0.9477 & **0.9673** \\ LA-MobileNet & 0.9609 & 0.9515 & **0.9547** & 0.9602 \\ \hline \hline \end{tabular} The results in bold mean the best performance under different metrics.  
\end{table}
Table 2: Experimental results of different models on the GTOS-mobile8 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Recall** & **F1-Score** & **Precision** \\ \hline MobileNetV3 & 0.9335 & 0.8881 & 0.8941 & 0.9241 \\ MobileNetV3+Auxloss & 0.9515 & 0.9386 & 0.9304 & 0.9439 \\ MobileNetV3+CA & 0.9568 & 0.9274 & 0.9328 & 0.9537 \\ LA-MobileNet & **0.9609** & **0.9515** & **0.9547** & **0.9602** \\ \hline \hline \end{tabular} The results in bold mean the best performance under different metrics.  
\end{table}
Table 3: Ablation experimental results on GTOS-mobiles dataset.