## 3 Experiments and Analysis
### The Experiment of Improved Method Applied to AlexNet  
This article needs to further verify the generalizability of LAC Block and E_CBAM. To achieve the effect of control variables, we chose AlexNet as the basic model and used LAC Block, AC Block, E_CBAM, and CBAM for the structural transformation of AlexNet. We added the WTB surface damage classification layer at the last layer; see Figure 11. The training mode with random model initialization parameters was adopted. Each model was trained 10 times, with 15 epochs per training, and tested after every epoch. The final and average accuracy, recall, precision, and F1 score were recorded. The results of six experiments are shown in Table 2.  
As shown in Table 2, both LAC Block and E_CBAM have been effectively improved. According to the analysis of experiment a and experiment f, AlexNet improved by LAC Block and E_CBAM not only improves the accuracy as well as other evaluation metrics significantly but also reduces the number of parameters. According to the analysis of experiment b and experiment c, LAC Block's accuracy rate increased by 0.43%, recall rate increased by 0.71%, accuracy increased by 0.36%, and F1 score increased by 0.54% compared with AC Block. It is worth noting that the AC Block replacement brings more parameters to AlexNet, but the LAC Block replacement reduces the original number of parameters. LAC Block is lightweight. The quantity of network training parameters is decreased by the use of depthwise separable convolution, which inevitably has a negative impact on the accuracy of the model and other performance indicators. However, LAC Block's combination of channel shuffle and depthwise separable convolution enhances the exchange of information between different groups of channels, making up for the loss. This means that the accuracy and other evaluation metrics do not decrease, the number of parameters can be reduced, and it is a lightweight network. From the analysis of experiment  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experimental** & **CNN** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **F1 Score** & **Params/M** \\ \hline a & AlexNet & 85.36 & 84.49 & 86.41 & 85.44 & 58.2936 \\ b & AlexNet + AC Block & 86.29 & 84.55 & 86.69 & 85.61 & 63.8156 \\ c & AlexNet + LAC Block & 86.72 & 85.26 & 87.05 & 86.15 & 55.9607 \\ d & AlexNet + CBAM & 89.41 & 87.83 & 88.27 & 88.05 & 58.3018 \\ e & AlexNet + E\_CBAM & 91.68 & 90.49 & 92.53 & 91.50 & 58.3020 \\ f & AlexNet + LAC Block + E\_CBAM & 94.46 & 93.15 & 94.63 & 93.88 & 55.9690 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experimental results based on AlexNet.  
Figure 10: LACB_ECBM Net’s accuracy growth at different batch sizes: (**a**) 16, (**b**) 32, and (**c**) 64.  
d and experiment e, E_CBAM is 2.27% more accurate than CBAM, with 2.66% more recall, 4.26% more precision, and 3.45% better F1 score. This indicates that E_CBAM is more targeted than CBAM in the task of fan blade surface damage identification. The difference between the cracks and surface shedding is small, but the depth, shape, and length of intraclass features vary greatly. In the E_CBAM, on the one hand, DPCI_SE Block enables the selection of channel information to obtain more abundant local and global information and ensure more sufficient feature dimensions, so that texture features such as cracks can be perceived more effectively. On the other hand, the application of the selective kernel in SK_SAM forms a multi-scale adaptive receptor field, allowing an appropriate-sized convolution kernel to extract features of that size, which enables complex features to be captured more efficiently in space.  
In conclusion, the combination of DPCI_SE Block and SK_SAM strengthens the selection of effective features and the suppression of ineffective or weak features for the surface damage characteristics of WTB. Even though there is a modest rise in the total amount of parameters, performance improves considerably as a result.