## 4 Grounding Grid Imaging Results and Evaluation Analysis  
### Ground Grid Imaging Results  
After the dataset was constructed, 100 rounds of training were conducted. Figure 9 shows the loss curves of the generator and discriminator during the training process. The generator loss is composed of adversarial loss and L1 regularization, while the discriminator loss reflects the performance when distinguishing between real samples and generated samples. The curve shows stable convergence at around 50 rounds of training, indicating that the training is balanced and feature learning is effective, without problems such as mode collapse or over-strengthening of the discriminator. In order to verify the effectiveness of the algorithm, this paper carried out experimental verification on a large number of test sets of grounding grid boundary voltages, analyzed the potential maps of different corrosion locations and corrosion degrees, and compared the conductivity topology maps obtained by the Tikhonov algorithm, NORSE algorithm, TV algorithm and CGAN imaging algorithm. A representative corrosion model was selected for simulation imaging, and the conductivity of the corrosion site was \(2.0\times 10^{6}\) S/m. The imaging effect is shown in Figure 10. According to the definition of conductivity value of image color mapping above, red, yellow and blue in turn represent the decrease of conductivity. The bluer represents lower conductivity, which is usually corrosive location or land. It is not difficult to find that the traditional EIT algorithm struggles to accurately  
Figure 8: Training flow chart. determine the corrosion location due to its morbidity and underdetermination when solving nonlinear inverse problems. The branch edge of the conductivity topology map presented in its imaging is poor, and there are artifacts in the branch. The actual conductivity difference between the corrosion site and the soil cannot be distinguished. It can only roughly reflect the corrosion location, and it is even more difficult to determine the degree of corrosion. Although the imaging of the improved CGAN algorithm proposed in this paper has conductivity color errors and a small amount of noise, it can accurately determine the corrosion location. The proposed CGAN-based approach introduces moderate complexity during the training phase, and once training is complete, its inference time is significantly faster than that of conventional methods. The significant improvement in reconstruction quality and noise immunity shown in Table 1 justifies the increased complexity, making it a viable approach for corrosion imaging of actual ground grids. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Reasoning Time (s)** & **Performance** \\ \hline Tikhonov & 6.3 & Poor quality, sensitive to noise \\ Noser & 4.5 & Moderate quality, limited robustness \\ TV & 7.8 & Good quality, prone to artifacts \\ CGAN & 2.3 & Excellent quality, robust to noise \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison table of algorithm complexity. Figure 10: Comparison of traditional algorithm imaging. Figure 9: Generate adversarial training loss graph. In order to verify the improvement effect of the improved CGAN algorithm, experimental comparisons were made with the generative network using CNN and U-net. Here, 100 rounds of training were conducted under the same conditions. The test set used typical single corrosion and double corrosion models. The imaging effect is shown in Figure 11. The green part of model 1 and 3 represents grounding grid corrosion, and the conductivity of the actual corrosion part is \(6.1\times 10^{6}\) S/m and \(5.3\times 10^{6}\) S/m. Model 2 is corroded at the blue position, and the conductivity at the actual corrosion position is \(2.8\times 10^{6}\) S/m. It can be seen that the improved CGAN imaging algorithm has made great progress in the diagnosis of grounding grid corrosion.