## 5 Results and Discussion
### Comparison of Visualization Effects  
One of the primary reasons why machine learning, especially deep learning, has not gained widespread trust is due to the fact that the inner workings of the model are often deemed an \"invisible black box.\" To address this concern, researchers have proposed a range of class activation mapping methods to analyze the decision-making criteria of the model. In this paper, the gradient-weighted class activation mapping (Grad-CAM) [56] method is employed to visually compare multiple models. Specifically, five samples from each category are randomly selected from the testing dataset and input into the model. The feature information after the last convolution is visualized. The darker the color of the red area within the activation map, the more important that area is deemed for decision-making. Firstly, we focus on visualizing the DenseNet121-DSC model, which is compared to ResNet18 and DenseNet121: two commonly used models with the best performance. Figure 16 shows the comparison. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Accuracy/\%** & **Loss** & **F1-Score/\%** & **Parameters** & **Model Size/MB** & **FLOPs** \\ \hline ShuffleNet\_v2 & 91.50 & 0.10 & 91.64 & 2,483,749 & 9.70 & 395.3 M \\ SqueezeNet & 74.50 & 0.97 & 75.11 & 737,989 & 2.82 & 979.95 M \\ MobileNet\_v3\_small & 90.00 & 1.22 & 90.03 & 1,522,981 & 5.96 & 76.64 M \\ Dense-DSC-0 & 97.00 & 0.03 & 97.00 & 245,055 & 1.11 & 368.01 M \\ Dense-DSC-SE & 98.50 & 0.13 & 98.51 & 248,551 & 1.13 & 368.01 M \\ Dense-DSC-ECA & 97.00 & 0.05 & 96.99 & 245,060 & 1.12 & 368.01 M \\ Dense-DSC-SAM & 99.00 & 0.04 & 99.00 & 245,074 & 1.11 & 368.01 M \\ Dense-DSC-CAM & 97.50 & 0.04 & 97.51 & 252,047 & 1.14 & 368.02 M \\ Dense-DSC-CBAM & 99.50 & 0.05 & 99.50 & 252,066 & 1.14 & 368.02 M \\ K nearest neighbors [28] & 76.50 & - & 74.40 & - & - & - \\ SVM [28] & 79.40 & - & 77.70 & - & - & - \\ Random forest [28] & 82.60 & - & 79.40 & - & - & - \\ CNN [28] & 87.00 & - & 87.40 & - & - & - \\ LSTM [28] & 81.70 & - & 81.00 & - & - & - \\ BiLSTM [28] & 80.10 & - & 79.70 & - & - & - \\ WPT-CNN [29] & 99.78 & - & 99.76 & - & - & - \\ CNN [30] & 98.20 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation metrics of lightweight models and models with different structures. Figure 16 shows that the concentration of focus areas for all three models is within the lower left corner of the SST image, an area with low-frequency where both the blind zone and defect signals are present in the time domain. However, these models' red areas are slightly focused toward the upper right with only a partially yellow-green area near the lower boundary. This area is the primary area of concentration for the blind zone and defect signal, indicating that it is important for these models but not weighted heavily. Out of the three models, only ResNet18 focuses on PDBZ and GPD located in the lower left corner and are physically significant near the lower boundary. Finally, the same five pictures are utilized to visualize three models with different attention mechanisms, and the results are presented accordingly. Figure 17 reveals that the focus areas for Dense-DSC-0 and Dense-DSC-SE are similar to that of DenseNet121. While the area of the blind zone signal and defect signal is also observed, they are depicted only in yellow and green with the overall red area leaning towards the upper right. Notably, the focus area for Dense-DSC-CBAM lies close to the lower boundary, signifying its emphasis on low-frequency regions. The red range encompasses the entire area from the blind zone signal to the defect signal outside the blind zone. Furthermore, the yellow-green transition area for Dense-DSC-CBAM is narrower than that seen in other models, which highlights the effectiveness of the CBAM module in enhancing the model's focus on critical time-frequency feature information of SST and refining the model's focus area. Figure 16: Focus areas of ResNet18, DenseNet121, and DenseNet121-DSC.