## 4 Grounding Grid Imaging Results and Evaluation Analysis
### Analysis of Ground Grid Imaging Evaluation  
The conductivity value of each branch can be calculated by Equation (18), and then the corrosion situation can be further obtained. According to the corrosion situation after imaging of model 3 in Figure 13, it can be concluded that branch 2 and branch 24 are severely corroded. The conductivity of branch 2 is calculated to be \(1.8\times 10^{6}\) S/m, and its corrosion degree is 82%. The conductivity of branch 24 is \(1.5\times 10^{6}\) S/m, and its corrosion degree is 85%. To further verify the grounding grid corrosion detection performance of this algorithm on different model test sets, the following figure shows a randomly selected part of the test data. The scatter plot of the measured corrosion degree \(\xi_{x}\) at the corroded location and the actual corrosion degree \(\xi_{y}\) at the corroded location are shown in Figure 14. It can be concluded that the corrosion degree of the corroded branch of the grounding grid detected by it is basically consistent with the actual one, and the error is small. This verifies that the proposed algorithm is highly accurate in assessing the degree of corrosion and shows that it can reliably detect corrosion in grounding grid systems with small errors, confirming its robustness and practical applicability in real-world scenarios. Then, the grounding grids were imaged with different grid sizes and single-branch corrosion and double-branch corrosion to study the influence of corrosion position identification accuracy and corrosion degree error. From the results of Table 2, it can be concluded that the improved CGAN imaging method proposed in this paper has the highest accuracy when the corrosion branch is a single branch in the single corrosion case of the \(2\times 3\) and \(3\times 3\) grids, and it needs to be improved under the double branch. In the case of double-branch corrosion, the different corrosion degrees lead to subtle changes in potential characteristics, making it difficult to extract features and thus increasing the error in imaging corrosion degree and decreasing the accuracy of corrosion position judgment. Compared with the traditional EIT imaging method, this method still has great advantages and is conducive to the application of grounding grid corrosion diagnosis. Figure 14: Comparison of measured corrosion levels. Figure 13: Corrosion of each branch of model 3. In Table 3, the evaluation index table of different algorithms shows that the average \(P\) of the image generated by the improved CGAN is 1.7407 higher than that of the image generated by U-Net, and it is 3.4809 higher than that generated by the CNN. The SSIM value is increased by 0.0445 and 0.0904 compared with U-Net and the CNN. This fully demonstrates the effectiveness of the improved method in better retaining accurate feature representations such as structural details. The increased PSNR and SSIM values indicate that the proposed method and its improved generator can provide more accurate and reliable conductivity images for grounding grid analysis, and thus they may play a greater role in the study of grounding grid conductivity imaging. The accuracy \(A\) of the corrosion branch judgment of each algorithm on the test set is calculated, and the \(R\) between the corrosion degree of the corrosion site and the actual degree is obtained, as shown in Table 4. It can be concluded that the improved CGAN imaging algorithm proposed in this paper has an accuracy increase of 9.1% compared with the CNN structure after improving the generation network, and the corrosion degree judgment error is reduced by 15.4%. Compared with the U-Net network, the accuracy is increased by 5.7% and the error is reduced by 8.8%.