Improving Infrared Small Target Detection With GAN-Driven Data Augmentation

Infrared small target detection (IRSTD) based on deep learning has received extensive research and application. However, deep learning models require a large amount of data to perform well, and the collection and standardization of infrared small target data is challenging, limiting the applicability of such models. To address this issue, this study proposes a data augmentation scheme for infrared small targets based on Generative Adversarial Networks (GANs). The proposed method is a two-step approach: the first step is the generation of clean backgrounds, and the second is the adaptive fusion of targets and backgrounds. In the background generation stage, we first use the Fast Marching Method (FMM) to fill background targets and obtain clean backgrounds. Then, we design a multi-generator and multi-discriminator GAN model (MGD-GAN) to generate high-quality and diverse background images. In the adaptive target-background fusion stage, we propose a dual-discriminator GAN network (FusionGAN), which allows the target mask to be adaptively fused with the background pixels. By combining real targets with generated backgrounds, new infrared small target images are generated, achieving the goal of data augmentation. Experiments conducted across three different scenarios demonstrate that the proposed data augmentation scheme effectively enhances the performance of both traditional and advanced detection models.