InfraRed Small Target Detection （IRSTD） aims to find targets from infrared images with low Signal-to-Clutter Ratio （SCR） accurately， and has been used in many fields widely. However， due to the weak target features and severe background interference， the existing methods struggle to extract structural information of the target effectively， which leads to issues such as incomplete target segmentation and low detection accuracy. Moreover， these models usually have a large number of parameters. To solve the above problems， a dual-coding space-frequency mixing IRSTD method was proposed. Firstly， with using U-Net3+ as the basic framework， a dual-coding structure combining Multi-Shape Context Aware （MSCA） module and Frequency-Domain Interactive Attention （FDIA） module was proposed to extract space-frequency mixing features in the coding stage. Secondly， in the decoding stage， a Cross-Layer Feature Guide （CLFG） module was designed to fuse multi-scale feature maps. Finally， the proposed method was experimentally verified on the NUAA-SIRST and IRSTD-1k datasets. The results show that the proposed method has the number of parameters of 0.86×106， and the Intersection over Union （IoU） reached 78.11% and 69.08% respectively. Compared with Attention Multiscale feature Fusion U-Net （AMFUNet） method， the number of parameters is reduced by 1.31×106， and the IoUs are increased by 2.25 percentage points and 1.23 percentage points respectively. It can be seen that the proposed method has high detection performance while retaining fewer parameters.
