## 3 Experiments and Analysis
### Validation Experiments on the Effectiveness of Improvements  
Because the EAC Block and DPCI_SE Block proposed in this paper are enhanced versions of the AC Block and SE Block, the validity of the enhancement must be confirmed.  
To verify the validity of the EAC Block proposed in this paper, a pair of CNNs are developed, with the only structural difference between the EAC Block and AC Block. Similarly, to verify the validity of the DPCI SE Block proposed in this paper, another pair of CNNs models is developed, with the only structural difference between the DPCI_SE Block and the SE Block. We trained and tested these two CNN pairs repeatedly, and the final results are averaged. The structure of the four CNNs is shown in Figure 11.  
Table 3 displays four experimental results in total. In terms of accuracy analysis, both the EAC Block and the DPCI_SE Block have an improvement effect, with the EAC Block improving by 0.35% over the AC Block and the DPCI_SE Block improving by 2.05% over the SE Block. In the recall analysis, the EAC Block outperformed the AC Block by 0.45%, while the DPCI_SE Block outperformed the SE Block by 1.59%, indicating that, for identifying complex wind turbine blade damage features, the method of enhancing effective information and suppressing invalid information by enriching spatial location information is more effective than the method of increasing the weight of convolutional kernel skeleton locations. In conjunction with the analysis of the parameter volume, the EAC Block increases the number of parameters more than the DPCI_SE Block, but the enhancement effect on the accuracy and F1 score is not as good as the DPCI_SE Block, indicating that the EAC Block's enhancement efficiency is inferior to that of the DPCI_SE Block and that further improvement is required. [PERSON], [PERSON], and [PERSON]. [31] use asymmetric convolution to enhance depthwise separable convolution for pest classification. Their ideas inspired us to improve the EAC block with lighter technologies that work better. Nonetheless, the overall increase in the number of parameters introduced by both modules falls within an acceptable range.  
Furthermore, it is important to note that the DPCI_SE Block structure is similar to the CAM structure in CBAM [32]. In CAM, the feature map first traverses the dual pooling parallel structure, then the MLP, and then the summation operation. In contrast, in DPCI_SE Block the feature map is fed into the SE block after passing through the dual pooling parallel structure and the splicing operation. We attempt to replace the DPCI SE Block in ED Net with CAM and conduct multiple experiments to determine the mean value; the resulting data are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **F1 Score** & **Params/M** \\ \hline CNN By EAC Block & 95.12 & 94.69 & 95.36 & 95.02 & 3.99 \\ CNN By AC Block & 94.77 & 94.24 & 94.73 & 94.48 & 3.08 \\ CNN By DPCI_SE Block & 97.42 & 97.26 & 97.11 & 97.18 & 3.15 \\ CNN By SE Block & 95.37 & 95.67 & 94.86 & 95.26 & 2.87 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of the four network modules.  
According to experimental results, for the wind turbine blade damage dataset, the number of parameters of CAM is less than that of the DPCI_SE block, but the accuracy rate is slightly lower than that of the DPCI_SE block, which is rich in fine texture features and requires classification of pictures at the pixel level. Therefore, the required number of features is substantial, and the number of channels in the expanded feature map is crucial. This could be one of the reasons why concat is more efficient than add.