## 3 Experimental Results and Analysis
### Validation of the Effectiveness of the Rail Surface Condition Identification Algorithm  
#### 3.3.1 Validation of the Effectiveness of Transfer Learning  
To evaluate whether transfer learning enhances the training performance of both the teacher model and the student model, Group 1 ablation experiments were conducted. The validation set accuracy and loss for both models, before and after applying transfer learning, are illustrated in Figure 9. Model performance was further assessed on the test set, with key evaluation metrics summarized in Table 3. The results demonstrate that transfer learning  
Figure 7: Partial image of the dataset: (**a**) dry rail surface; (**b**) wet rail surface; (**c**) oily rail surface.  
significantly accelerates network convergence and improves accuracy. This improvement is attributed to the pre-trained model initialization, which allows the earlier layers of the network to inherit well-optimized parameters, bringing them closer to the optimal solution early in the training process. Additionally, since all layers undergo transfer learning, the model can extract finer-grained feature details from images, leading to higher identification accuracy. A comparative analysis between the GoogLeNet (teacher) model and the MobileNet (student) model further confirms that both models exhibit substantial performance improvements after applying transfer learning. In terms of loss convergence, both GoogLeNet and MobileNet demonstrate enhanced learning capabilities following transfer learning. The models subjected to transfer learning exhibit a lower initial loss value, with a rapid decline and faster stabilization. In contrast, models without transfer learning present a higher initial loss and experience significantly greater fluctuations throughout the training process. Regarding evaluation metrics, including accuracy, precision, recall, and F1 score, GoogLeNet and MobileNet after transfer learning outperform their counterparts without transfer learning. Notably, MobileNet shows a more pronounced optimization effect. This is further corroborated by the accuracy curve, which indicates reduced accuracy fluctuations and improved feature extraction from the dataset. In terms of inference time, the difference between GoogLeNet and MobileNet after transfer learning is either minimal or shorter. Specifically, MobileNet achieves a 1.24% reduction in inference time. Although the reduction is relatively modest, the simultaneous improvement in accuracy underscores the effectiveness of transfer learning. Furthermore, the number of parameters in both GoogLeNet and MobileNet is significantly reduced after transfer learning. GoogLeNet experiences a 15.41% reduction in parameter count, while MobileNet achieves a 23.18% reduction. This reduction in parameters further enhances the model's lightweight nature, contributing to improved efficiency and performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy/\%**} & \multirow{2}{*}{**Precision/\%**} & \multirow{2}{*}{**Recall/\%**} & \multirow{2}{*}{**F1/\%**} & \multirow{2}{*}{**Model Inference**} & \multirow{2}{*}{**Number of**} \\  & & & & & **Time/s** & \\ \hline GoogLeNet & 96.05 & 96.15 & 96.05 & 96.05 & 0.0404 & 6.62 \\ GoogLeNet-Rail & 96.90 & 96.93 & 96.90 & 96.91 & 0.0422 & 5.60 \\ MobileNet & 86.89 & 88.66 & 86.89 & 86.96 & 0.0402 & 5.48 \\ MobileNet-Rail & 95.40 & 95.58 & 95.40 & 95.38 & 0.0397 & 4.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the transfer learning experiment.  
Figure 8: Co-simulation flowchart.