Global attention network with multiscale feature fusion for infrared small target detection

A global attention network (GANet) with multiscale feature fusion is proposed to detect infrared small target by introducing a transformer attention module and an adaptive asymmetric fusion module. The transformer attention module is designed to learn the long-range relationship between small targets and background. The adaptive asymmetric fusion module is employed to aggregate the multiscale contextual information from highlevel and low-level features. In addition, a target duplicating data augmentation strategy by copy-pasting small targets many times is proposed to increase the positive samples during training for suppressing the classimbalance problem. Extensive experiments on infrared small target datasets demonstrate that our method can achieve high detection accuracy and low false alarm rate compared with some state-of-the-art model-driven and data-driven methods.