Aiming at the issues of insufficient feature representation capability of latent vectors for positive samples, suboptimal reconstructed image quality by the decoder, and inadequate discriminative ability of the discriminator in the GANomaly model, a tire X-ray image defect detection method based on D2GANomaly is proposed. First, a multi-scale dynamic residual block (MDRB) is introduced into the encoder, which combines adjustable kernel convolution (AKConv) with residual connections to dynamically fuse multi-scale features and enhance fine-grained feature extraction capabilities. Second, a channel residual sub-pixel decoder (CRSD) is incorporated into the decoder section, utilizing dual decoders for parallel learning to optimize the reconstruction quality of complex textures and details. Finally, the discriminator employs a dual discriminative module network (DDMN), which uses switchable atrous convolution (SAC) to select the optimal dilation rate, thereby enhancing the model’s ability to detect defects of varying sizes in tire X-ray images and improving its discriminative performance. Experimental results demonstrate significant improvements in two core performance metrics, Area under the receiver operating characteristic curve (AUC) and average precision (AP). Compared to the original GANomaly model, the proposed method achieves a 13.7% increase in AUC and a 16.4% increase in AP. This indicates that the improved model effectively enhances the accuracy of tire defect detection.