## 4 Experimental Verification
### Pressure Relief Hole Identification Test
#### 4.3.2 Results and Analysis  
1. Test I: Explore the influence of SqueezeNet improvement measures and BN normalization on the recognition model. The original SqueezeNet, the improved SqueezeNet, and the improved SqueezeNet plus BN normalization layer were used as the basic networks to train the recognition model. The change curves of the loss function are shown in Figure 12. As can be seen from the figure, with the increase in the number of training iterations, the loss function showed a downward trend as a whole. The loss decreased significantly in the early stage of training, but decreased slightly and tended to be stable in the later stage of training. By comparing the three convergence curves, it can be seen that the BN normalization layer was able to make the curve converge faster, restrain the oscillation amplitude, and reduce the convergence value. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Setting Values** \\ \hline iterations & 150,000 \\ anchors & 5 \\ max\_boxes & 150 \\ base\_lr & \(1\times 10^{-5}\) \\ cnot & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The setting values of hyperparameters. Figure 11: Training duration of different SinGAN models [25]. Figure 13 shows the accuracy rate changes of different SqueezeNet models during 10-fold cross-validation. As can be seen, the average accuracy rate of the improved SqueezeNet + BN model during 10-fold cross-validation was the highest, and the volatility was the lowest, which can verify its stability. The specific target recognition results of relief holes based on the above three basic network models are shown in Table 3. It can be seen that the target recognition effect of the relief hole could be significantly improved by increasing the number of convolution layers and placing the maximum pooling layer backward, with the accuracy rate and recall rate increased by 8.8% and 3.64%, respectively. Secondly, adding the BN layer could also enhance the target recognition effect of the relief hole, with its accuracy increased by 3.41% and recall increased by 4.52%. By comparing the average training time under different basic networks, it can be found that the training time of the improved SqueezeNet + BN model increased slightly, which indicates that the number of convolution layers affects the convergence speed of model training. Figure 12: The loss function curves of different SqueezeNet models: (**a**) classification loss of fully connected layer; (**b**) regression loss of the fully connected layer. Figure 13: The accuracy rate changes of different SqueezeNet models during 10-fold cross-validation. 2. Test II: Explore the influence of multi-scale image input and multi-layer feature fusion on the recognition model. Taking the improved SqueezeNet + BN as the basic model, the multi-layer feature fusion strategy was, firstly, added for training. On this basis, the multi-scale image input strategy was then added to train the recognition model. The change curves of loss function are shown in Figure 14. The convergence curves of the two training methods eventually converged around 0.2, and the convergence curves basically coincided with each other. The difference was relatively small. The target recognition results of the pressure relief hole based on the above two training methods are shown in Table 4. By comparing Tables 3 and 4, it is not difficult to find that the accuracy and recall rate have been greatly increased by 2.03% and 16.05% after adding multi-layer feature fusion. However, the average training time increases from 160 s to 238 s, reducing the calculation efficiency of the recognition model. In addition, it can be seen from Table 4 that the recall rate of the recognition model has been increased by 2.04%, although the index values of accuracy and training time have slightly deteriorated by simultaneously using the multi-level feature fusion and image multi-scale input strategy.