To address the challenges of low contrast and small defect sizes in some X-ray images of tires, which make detection difficult, an improved model based on generative adversarial networks (GANs) is proposed to enhance the accuracy of tire defect detection. Initially, issues with traditional generators are analyzed. Building upon the GANomaly model, the proposed approach incorporates the attention mechanism module (NAM), flow alignment module (FAM), and PatchGAN to enhance feature extraction and image reconstruction capabilities. The NAM enhances the model’s focus on defect areas through normalization, while the FAM accurately maps features from low-resolution to high-resolution feature maps, ensuring information consistency and effective fusion across multiple scales. PatchGAN, with its local discriminator, improves the model’s ability to recognize local features. Validation tests on a self-constructed dataset of four tire defect types demonstrate significant improvements in key metrics, achieving an AUC of 96.4% and an AP of 95.8%. These results indicate enhanced feature extraction and image reconstruction capabilities, leading to improved defect detection accuracy.