Grounding grid, as an important equipment to ensure the safety of power system, the research on its corrosion state detection is of great significance. Electrical impedance tomography is one of the important methods for grounding grid corrosion imaging. Due to its pathological nature when solving the inverse problem, the reconstruction effect has a large deviation. In order to improve its imaging quality and accuracy, this paper proposes a total variation- conditional generative adversarial network (TV-CGAN) algorithm to detect its corrosion state. First, the grounding grid forward problem model is established to solve the boundary voltage, and then the total variation (TV) regularization algorithm is used to solve the inverse problem to obtain a preliminary grounding grid conductivity distribution image. Then, the conditional generative adversarial network algorithm is used to perform secondary imaging on the image obtained by the TV method. The generator is a U-Net structure that introduces the convolutional block attention module. The discriminator is a PatchGAN convolutional structure. This method was applied to the detection of grounding grid corrosion status. The reconstructed image structure similarity result was 0.907 8, the peak signal-to-noise ratio was 16.935 6, the corrosion position judgment accuracy was 96.35%, and the corrosion degree judgment error was 8.61%. The results show that this method effectively improves the ill-conditioned problem in solving the inverse problem, improves the quality of grounding grid corrosion imaging, and improves the accuracy of grounding grid corrosion detection.