CDMNet: Contrastive Distribution Mapped Network for Infrared Small Target Detection

Single-frame infrared small target (SIRST) detection is an extremely challenging task due to its low signal-to-noise ratio and low contrast. Previous methods fail to achieve promising performance as they do not consider the analogous and blurred background surrounding. To this end, we first propose a prototype-based contrastive loss (PCL) by modeling the foreground targets and the surrounding nearest backgrounds. As a result, the prototypes of different categories in the latent space could be far away, which enables the model to make clear decisions on the boundaries of infrared small targets. Moreover, previous methods neglect the distribution inconsistency caused by feature fusion in U-shaped architecture. Therefore, we design a multi-scale distribution-mapped fusion (MDMF) module, which greatly mitigates the distribution inconsistency issue. We incorporate the proposed PCL and MDMF module into the existing SIRST detection method to construct a new SIRST detection framework called Contrastive Distribution Mapped Network (CDMNet). Extensive experiments on two infrared small target datasets, NUDT-SIRST and IRSTD-1k, demonstrate that our model outperforms current competitive models on a variety of metrics.