Infrared Tiny Object Detection Using Semi-Supervised Learning and Data Augmentation

Abstract

Aiming at the core challenges of data shortage and high labeling cost in the infrared small target detection task, this paper puts forward an innovative method of integrating generative adversarial network and semi-supervised learning. By designing the small target generative adversarial network (STGAN) based on StyleGAN2, combining the self-attention mechanism and the comparative learning loss, the quality and diversity of generated data are effectively improved. At the same time, the knowledge distillation framework is used to optimize the student model by using the pseudo-tags generated by the teacher model, which significantly improves the detection performance in small sample scenes. The experimental results show that the nIoU (1% labeled data) of 0.709 is achieved by the proposed method on SIRST data set, and the FID index is reduced to 0.45, both reaching the current optimal level. The ablation experiment further verified the key roles of detection loss, self-attention module and REP-PAN feature fusion strategy, and provided a new idea for data expansion and model optimization in the field of small target detection.