Unmanned　aerial　vehicles　(UAVs)　and　infrared　imaging　technology　have　numerous　applications　in　civilian　fields.　To　address　the　issues　of　low　accuracy　resulting　from　complex　ground　backgrounds,　small　target　size,　and　limited　target　features　in　UAV　remote　sensing　infrared　image　target　detection,　we　use　the　YOLOv9s　model　and　the　latest　retentive　networks　meet　vision　transformers　(RMTs)　technology　and　propose　the　RMT-YOLOv9s　model　for　infrared　small　target　detection.　First,　a　convolutional　neural　network　(CNN)-RMT-based　backbone　is　proposed　by　incorporating　the　RMT　model　into　the　backbone　network　of　YOLOv9s,　which　extracts　both　local　and　global　features　for　small　target　detection.　Then,　an　improved　neck　multiscale　feature-fusion　network　RMTELAN-PANet　is　designed　using　the　novel　convolutional　RMTELAN　module　proposed　in　this　letter,　which　can　better　capture　and　use　semantic　information　from　feature　maps.　Finally,　efficient　multiscale　attention　(EMA)　attention　module　and　upsampling　Dysample　module　are　integrated　into　RMTELAN-PANet　to　further　improve　the　feature　information　of　small　targets.　Experiments　on　the　HIT-UAV　dataset　show　that　RMT-YOLOv9s　outperforms　other　popular　methods　in　infrared　small　target　detection.