## 3 Experiments and Analysis
### Transfer Learning Experiments  
Transfer learning is the reuse of a previously trained model for a different task. In light of the fact that the ED Net model has fewer parameters and is a lightweight CNN model, comparable models include MobileNet_V1 [33], MobileNet_V2 [34], 1.0-SqueezeNext-23 [35], ShuffleNet_V1 [36], and ShuffleNet_V2 [37]. Figure 10 shows their respective primary building blocks and overall structures.  
The five pre-trained models are developed experimentally to perform migration learning on the wind turbine blade damage dataset. Each model is trained for 30 epochs, and the final accuracy and training time were recorded. Experiments are conducted with g = 3 because the accuracy of ShuffleNet_V1 is balanced concerning the number of parameters when the number of groups is equal to 3. The experimental result is in Table 5.  
As could be seen from Table 5, in terms of accuracy, recall, precision, and F1 index, the ED Net proposed in this paper outperforms the MobileNet_V1, MobileNet_V2, SqueezeNext-23, and ShuffleNet_V1 models on the wind turbine blade damage dataset. MobileNet-V1 and MobileNet_V2 have a greater number of parameters than ED Net. Therefore, the ED Net model can partially replace both of them in the identification of wind turbine blade damage. The first four metrics of the 1.0-SqueezeNext-23 model are inferior to those of the ED Net model, but the model has a small number of parameters and a remarkable training time. ShuffleNet_V2 has slightly higher accuracy than ED Net, a smaller number of parameters, and a shorter training time, which indicates the drawback of the proposed model: the number of parameters is slightly higher than the mainstream lightweight model. In addition, the training time still needs to be improved compared to the broad learning system model [38].  
This motivates us to implement the improved method of depthwise separable convolution with channel re-shuffling in our future research to reduce the number of parameters and computations of ED Net. However, ShuffleNet_V2's recall is slightly lower than that of ED Net. In a real task scenario, our goal is to identify all turbine blades with damage. Therefore, we prefer to implement a small number of manual inspections rather than miss every damaged feature of a wind turbine blade. Consequently, ED Net's higher recall is an advantage. To sum up, the ED Net model proposed in this paper balances the accuracy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **F1 Score** & **Params/M** \\ \hline ED Net & 95.12 & 95.31 & 95.42 & 95.36 & 3.99 \\ ED Net(c) 1  & 94.77 & 94.52 & 94.23 & 94.37 & 3.32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results of DPCI_SE Block and CAM experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Object** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **F1 Score** & **Params/M** & **Training Time** \\ \hline MobileNet\_V1 & 97.91 & 97.60 & 97.26 & 97.43 & 4.20 & 16 min 47 s \\ MobileNet\_V2 & 98.53 & 98.63 & 97.79 & 98.21 & 3.40 & 16 min 12 s \\
1.0-SqueezeNext-23 & 93.59 & 92.14 & 92.70 & 92.42 & 0.72 & 9 min 13 s \\ ShuffleNet\_V1(g = 3) & 98.51 & 98.46 & 97.59 & 98.02 & 2.40 & 16 min 23 s \\ ShuffleNet\_V2 & 99.48 & 98.86 & 99.36 & 99.11 & 2.3 & 15 min 19 s \\ ED Net & 99.23 & 99.43 & 99.34 & 99.38 & 3.39 & 16 min 06 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of experimental results of common lightweight CNNs.  
with the number of parameters and training time. Under the condition of high accuracy, the number of parameters and training time is still at an average level.