## 4 Results
### Analysis of Manhole Cover Classification Results  
In this paper, Mobilenetv1, Swin transformer_tiny and VGG16_BN networks were trained, and the results were shown in Table 5. Comparing the three networks, VGG16_BN had the best effect in the classification of manhole covers, the recognition accuracy of manhole covers was 97.62%, and the comprehensive score was the highest in the three networks, so VGG16_BN was selected as the classification network of manhole covers in this paper. \begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluating Indicator** & **SRGAN** \\ \hline PSNR & 29.54 \\ SSIM & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SRGAN reconstruction effect evaluation index table. Figure 16: Text positioning results display. (**a,b**) square manhole covers (**c**) stained covered manhole covers (**d,e**) sunny shooting manhole covers (**f**) mutilated manhole covers. After determining the image classification network, the VGG16_BN network undergoes K-value cross-validation to verify the consistency and reliability of the classification results. The verification results were presented in Table 6. It could be observed from the table that following five-fold cross-validation, the network demonstrated consistent performance, with an accuracy rate hovering at approximately 93.92%. Based on the K-fold calculation results, it could be concluded that at a 95% confidence level, the confidence interval of the classification accuracy was [0.93, 0.95] with a confidence coefficient of 1.96. This indicated the reliability of the results obtained from the VGG16_BN network. After determining the classification network, the hyperparameters need to be determined. In order to evaluate the influence of different learning rates on the model, this experiment used \(1\times 10^{-2}\), \(1\times 10^{-3}\), and \(2\times 10^{-5}\) to train the model for 150 epochs, and collected the loss function during the training process. Through Figure 18, it could be seen that the learning rate of \(1\times 10^{-2}\) is the fastest model convergence, and the model tends to be stable at 75 epochs. Therefore, the learning rate is set to \(1\times 10^{-2}\). The training batch is 100 epochs. Figure 17: Super-resolution reconstruction results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluating Indicator** & **Mobilenetv1** & **Swin_transformer_tiny** & **VGG16_BN** \\ \hline Precision & 92.73\% & 90.0\% & 97.62\% \\ Recall & 92.73\% & 94.32\% & 98.86\% \\ mAP & 93.75\% & 92.19\% & 98.44\% \\ F1\_Score & 92.73\% & 92.11\% & 98.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative evaluation metrics for well-cover categorization networks. After determining the parameters, the classification performance of the reconstructed manhole cover text images was validated. Under the condition of keeping other parameters consistent, the unreconstructed manhole cover text images and the reconstructed manhole cover text images were separately inputted into the network for training. By comparing the precision, recall, and top-1 accuracy metrics after training, the effectiveness of super-resolution reconstruction was evaluated. The final training evaluation metrics are presented in Table 7.  
It could be seen from Table 7 that all aspects of the VGG16_BN network trained with the super-resolution reconstructed manhole cover had been greatly improved. Among them, the recall rate of the image without super-resolution reconstruction was only 77.95%. After training with the super-resolution reconstruction data set, the recall rate had increased by 20%. At the same time, it could be seen from Figure 19 that even if it was tested on the original image, the network trained by super-resolution had also achieved a significant accuracy improvement. It could be concluded that the network trained by super-resolution reconstruction has a significant improvement in the accuracy of manhole cover classification.