Visual Ship Image Synthesis and Classification Framework Based on Attention-DCGAN

Abstract

To improving ship image generation and classification tasks, a deep convolution generative adversarial network based on attention mechanism (ADCGAN) model was constructed. The rectified linear unit (ReLU) activation function was adopted, and three Deconv layers and Conv layers were added to both the generator and discriminator. Subsequently, an attention mechanism was added to the generator, while spectral normalization (SN) was added to the discriminator. Mean squared error (MSE) was used as loss function to stabilize the training process. Furthermore, ship classification tasks were performed using the generated ship images by end-to-end training of the classification network, enabling ship data augmentation and co-learning with other tasks. Experimental results on the Ship700 and Seaship7000 datasets demonstrate that the ADCGAN model can generate clear and robust ship images, with PSNR, LIPIPS, MS-SSIM values of 20.279 and 27.523, 0.596 and 0.096, 0.781 and 0.947, respectively. The effectiveness of the proposed method in ship image classification tasks was also verified, providing a data foundation for other collaborative tasks.