Due　to　the　lack　of　infrared　image　data,　infrared　target　detection　is　still　a　challenging　task.　To　increase　the　performance　of　infrared　target　detection,　this　paper　proposes　a　feature　distillation　method　based　on　dual　attention　meta-network.　Specifically,　a　dual　attention　meta-network　is　designed　to　transfer　feature　knowledge　from　visible　to　infrared　images.　The　network　includes　two　parts:　Spatial　Attention　Meta-network　(SAM)　and　Channel　Attention　Meta-network　(CAM).　In　SAM,　the　distance　between　visible　and　infrared　features　is　minimized　by　designing　L2　loss,　so　that　the　knowledge　of　visible　features　contained　in　the　teacher　model　is　transferred　to　the　student　model.　In　CAM,　the　similarity　between　visible　and　infrared　features　is　calculated　as　the　distillation　strength　of　feature　connections　to　achieve　more　efficient　feature　distillation.　In　addition,　to　avoid　the　transfer　of　negative　information,　a　feature　selection　method　based　on　mutual　information　evaluation　is　proposed,　in　which　the　feature　connections　with　larger　mutual　information　are　retained　by　setting　a　mutual　information　threshold.　Experiments　on　public　infrared　detection　dataset　show　that　the　proposed　method　outperforms　the　state-of-the-art　feature　distillation　methods.　In　particular,　the　proposed　method　achieves　an　accuracy　of　94.35%　in　infrared　target　detection,　which　is　a　5.85%　improvement　compared　to　one　without　feature　distillation.　©　2024　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.