Nanetformer: Nested Attention Network With Auxiliary Transformer Enhancement for Infrared Small Target Detection

Infrared small target detection (ISTD) has been widely concerned in certain fields like astronomy, surveillance, and missile early warning system. ISTD is still a challenging task due to the complex backgrounds and small size of targets, which restrict the performance of the convolutional neural networks (CNN) in ISTD. To this end, a dual branch architecture which combines nested attention network and auxiliary transformer enhancement (NANetFormer) is proposed. The CNN-based branch uses channel-spatial-attention embedded U-Net++ architecture to obtain low-level local details of small targets and suppress background noises. The transformer-based branch applies hierarchical self-attention mechanism as an auxiliary enhancement encoder path. Furthermore, we design a local-global feature fusion module to make feature concentration of two branches. Experimental results show that proposed network achieves competitive results compared with other state-of-the-art methods.