Ship-Go: SAR Ship Images Inpainting via instance-to-image Generative Diffusion Models

Abstract

We present Ship-Go, an instance-to-image diffusion model, to increase the scale and diversity of the SAR detection datasets. Ship-Go is developed as a multi-conditions denoising diffusion probabilistic model (DDPM), i.e., it takes the proposed visual instances and environment prompt condition variables as constraints to generate backscatter intensity information for each resolution cell, resulting in inpainting a SAR image and generating the corresponding instance-level annotations, which can be directly employed to train detection models. We demonstrate, for the first time, the ability to place the ship objects at any angle, size, and arrangement in the generated background of multiple specified environment types. Importantly, two image generation scenarios are designed to increase the diversity of objects and backgrounds for the original dataset (i.e., in-domain augmentation and out-of-domain augmentation). The experiment verifies that the generated detection datasets boost the performance of multiple classical deep detectors in the different cases of insufficient samples. Qualitatively, we find that Ship-Go improves the diversity of the existing dataset, and has sample distribution transferability among multiple datasets. Code and models are available at: https://github.com/XinZhangRadar/Ship-Go.