Tire defect detection is of great significance for the identification of tire safety performance, and researching high-performance tire anomaly detection methods is extremely important for the safety performance of automobiles. This article proposes a network model, UDGANomaly, based on U-Net discriminators, which is based on generative adversarial networks. Firstly, encoding and decoding are introduced into the discriminator. The encoder module performs image-by-image classification, and the decoder module outputs pixel-by-pixel classification decisions, providing spatially coherent feedback to the generator. Secondly, a self-attention mechanism is introduced in the encoder and decoder of the generator to further focus on the representative information contained in multi-scale features. Finally, an improved generator loss function based on structural similarity was designed to address visual inconsistency and enhance the robustness of irregular texture detection. After comparative research, it was found that the network structure proposed in this paper has significantly better anomaly detection performance than other traditional network models on the same tire dataset, and the average testing accuracy is as high as 95.6%.