To address the difficulty associated with identifying the edge area in aluminum plate defect eddy current inspection images, in which background noise is typically problematic, an image segmentation method for aluminum plate defect eddy current detection based on improved generative adversarial network is proposed. The proposed method is based on the generative adversarial network image segmentation model. The generator partly adopts the idea of the U-Net model. Prior to the fusion of high- and low-level features, an attention module is used to adjust the weight of both low- and high-level features. This weight adjustment improves the utilization of image feature information, enhances the target features, and suppresses background features. The discriminator network is used to distinguish the results generated by the network and actual manually labeled results. The proposed method uses Precision, Recall, and F1 as evaluation indicators. Compared with the traditional image segmentation methods, the proposed method achieves a better segmentation effect for aluminum plate defect eddy current inspection images.