## 3 Experiments and Analysis
### Training of ED Net  
Cross entropy improved by label smoothing is used as the loss function during the training of the ED Net model. Adam gradient descent optimizer with good performance is used for optimization, attenuation rate \(\beta_{1}=0.9\), \(\beta_{2}=0.999\)[30], the batch size is set to 32, and the learning rate attenuation strategy with fixed step size is implemented. The initial learning rate is set to 0.001, and it is decreased to 0.6 times the current rate after every 10 epochs. As shown in Figure 10, a total of 30 epochs are trained, and each epoch is trained to record the accuracy and degree of loss of the current model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Identified as Cracks** & **Identified as Surface Shedding** & **Identified as Normal** \\ \hline Cracks & a & b\({}^{\prime}\) & c\({}^{\prime}\) \\ Surface shedding & a\({}^{\prime}\) & b & c\({}^{\prime\prime}\) \\ Normal & a\({}^{\prime\prime}\) & b\({}^{\prime\prime}\) & c \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix for surface damage identification.  
The convergence of accuracy and loss of the ED Net model during training is shown in Figure 9 as the number of training epochs increases. During the first 10 training epochs, the accuracy increased and the loss of the model decreased rapidly. Between 11 and 20 training epochs, there is a slight fluctuation in accuracy and a brief plateau in the loss. Possibly, the learning rate of the model during training at this stage is too high; therefore, it is necessary to adjust the learning rate to improve training performance. During the 20-round training period, the learning rate slows for a second time, and the model's accuracy shows a slow and steady increase, eventually converging at 99.138%. The degree of loss also exhibits a gradual downward trend, eventually converging to 9.4234 \(\times 10^{-3}\). ED Net is effective at identifying the surface damage of wind turbine blades, according to the convergence of the final model.  
It is worthy of discussion that the middle portion of ED Net's structure can be viewed as a tandem of identical synthetic structures. To investigate the impact of the number of synthetic structures in series on ED Net, a set of ED Net with varying numbers of synthetic structures is designed, as shown in Figure 11. Several experiments are conducted and recorded with the different number of series set to F; the outcomes are shown in Table 2.  
According to the experimental results presented in Table 2, the model's accuracy, recall, precision, and F1 score are all best when F equals 3. When F is greater than 3, the indexes are in an ascending and descending range and are all smaller than when F is equal to 3. We  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**F** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **F1 Score** & **Params/M** \\ \hline
2 & 97.91 & 97.63 & 97.49 & 97.56 & 5.40 \\
3 & 99.23 & 99.26 & 99.35 & 99.31 & 3.39 \\
4 & 99.02 & 98.86 & 98.73 & 98.79 & 3.18 \\
5 & 98.51 & 98.72 & 98.65 & 98.68 & 4.26 \\
6 & 99.16 & 98.66 & 99.13 & 98.89 & 4.73 \\
7 & 98.93 & 98.71 & 98.89 & 98.80 & 5.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Influence of the number of series connections on ED Net.  
Figure 11: The structure of a tandem of F synthetic structures.  
Figure 10: (a) The accuracy of ED Net; (b) the training loss of ED Net.  
investigate the cause of this phenomenon: the wind turbine blade damage dataset contains small images. Although the small size of the input images can reduce the computational effort of the model after the model continuously downsamples the feature maps, the size and resolution of the feature maps in the model's depth will be significantly reduced. Consequently, the deeper convolutional layers are unable to extract more effective feature information. Increasing the number of tandem synthetic structures is one of the reasons for this, but the accuracy cannot be improved effectively.