## 3 Experiments and Analysis
### The Experiment of Common Lightweight Networks Based on Transfer Learning  
Transfer learning is the successful application of image classification knowledge learned in large data sets to new target classifications to be solved. The model relies on the current optimal network model after transfer training, and the model in this article is constructed by fine-tuning the network layer structure, which is faster and easier than building and training a new randomly initialized network. To measure the overall performance of LACB_ECBAM Net, this article compares the common pre-trained lightweight models in the field of image recognition with LACB_ECBAM Net, including ResNeXt50 (32 \(\times\) 4d) [43], Xception, MobileNet_V2, ShuffleNet_V2, and EfficientNet-B0 [44]; their structures are shown in Figure 10. The pre-trained network weight parameters from the ImageNet data set were added to the aforementioned models to ensure that the experiment was fair, and the last layer was added to the WTB surface damage classification layer. Each model was trained 15 times, with each training being 20 epochs, and was tested after every epoch. The final and average accuracy, recall, precision, F1 score, and training time were recorded each time, and their average values were taken as the basis for experimental analysis in Table 3.  
Table 3 shows that the accuracy of the LACB_ECBAM Net proposed in this article is 99.94%, placing it top and ahead of Xception, which is ranked second by 0.08%. This represents a significant advantage in both LACB_ECBAM Net and Xception accuracy over other networks. Xception is significantly optimized at all levels compared to models in the same family. Then, in terms of recall and precision, Xception ranked first, higher than LACB_ECBAM Net. LACB_ECBAM Net's recall is 99.92%, which ranks first, higher than Xception's. In the identification of surface damage of WTB, LACB_ECBAM Net should be more inclined to identify all damaged WTB, preferring to spend a small number of human resources for secondary inspection. Therefore, a higher recall rate is also one of the advantages of LACB_ECBAM Net. Secondly, from the perspective of parameter quantity, the parameter quantity of LACB_ECBAM Net is only 0.58 M, which is the least among the models involved in the experiment. LACB_ECBAM Net replaces the huge fully connected \begin{table}
\begin{tabular}{c c c c c c c} \hline
**CNN** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **F1 Score** & **Params/M** & **Training Time** \\ \hline ResNeXt50 (32 \(\times\) 4d) & 98.63 & 98.19 & 97.42 & 97.80 & 24.42 & 21 min 52 s \\ Xception & 99.86 & 99.83 & 99.96 & 99.89 & 22.77 & 19 min 17 s \\ MobileNet\_V2 & 98.33 & 98.51 & 98.72 & 98.61 & 3.40 & 16 min 9 s \\ ShuffleNet\_V2 & 99.48 & 98.86 & 99.36 & 99.11 & 2.3 & 15 min 17 s \\ EfficientNet-B0 & 98.53 & 97.78 & 98.47 & 98.12 & 4.99 & 15 min 45 s \\ LACB\_ECBAM Net (ours) & 99.94 & 99.88 & 99.92 & 99.90 & 0.58 & 15 min 29 s \\ \hline \end{tabular}
\end{table} 
Table 3: Contrast experiment of common lightweight networks. layer with the global mean pooling layer, so it has an advantage in the number of parameters. In addition, most convolution in LACB_ECBAM Net is improved by depthwise separable convolution, which reduces the number of overall parameters. Finally, in terms of training time, LACB_ECBAM Net is at an average level, and the training time used is 15 min 29 s, which is slower than ShuffleNet_V2 because there are too many \"multi-path\" [41] structures designed in LACB_ECBAM Net, mainly in LAC Block. It is easy to cause network fragmentation, which will reduce the degree of parallelism of the model [41], and slow the corresponding speed, which is also the problem that LACB_ECBAM Net needs to solve in follow-up research. Additionally, the experimental indicators of LACB_ECBAM Net are stronger than ResNeXt50 (32 \(\times\) 4d), MobileNet_V2, and EfficientNet-B0.