In response to the problem of small differences in blister defect features and background pixels in tire defect images, as well as difficulty in detection, Skip-GANomaly is adopted as the basic framework to propose the fusion attention mechanism generative adversarial network (FAMGAN). Firstly, the skip layer between the encoder and decoder in the generator consists of an attention feature fusion (AFF) module and a convolutional block attention module (CBAM) module, which improves the focus on target features and reduces image feature loss. Then, a joint pyramid upsampling (JPU) module was added to the discriminator to improve the speed of the model in detecting image defects. Finally, the FAMGAN network proposed in this article will be trained, tested, and evaluated on a selfmade tire defect dataset with classic generative adversarial networks in recent years. The experimental results show that the proposed network achieves an accuracy of 0. 837 for tire blister defect detection, which is nearly 30 percentage points higher than the Skip GANomaly network.