Camouflaged　people　are　extremely　expert　in　actively　concealing　themselves　by　effectively　utilizing　cover　and　the　surrounding　environment.　Despite　advancements　in　optical　detection　capabilities　through　imaging　systems,　including　spectral,　polarization,　and　infrared　technologies,　there　is　still　a　lack　of　effective　real-time　method　for　accurately　detecting　small-size　and　high-efficient　camouflaged　people　in　complex　real-world　scenes.　Here,　this　study　proposes　a　snapshot　multispectral　image-based　camouflaged　detection　model,　multispectral　YOLO　(MS-YOLO),　which　utilizes　the　SPD-Conv　and　SimAM　modules　to　effectively　represent　targets　and　suppress　background　interference　by　exploiting　the　spatial-spectral　target　information.　Besides,　the　study　constructs　the　first　real-shot　multispectral　camouflaged　people　dataset　(MSCPD),　which　encompasses　diverse　scenes,　target　scales,　and　attitudes.　To　minimize　information　redundancy,　MS-YOLO　selects　an　optimal　subset　of　12　bands　with　strong　feature　representation　and　minimal　inter-band　correlation　as　input.　Through　experiments　on　the　MSCPD,　MS-YOLO　achieves　a　mean　Average　Precision　of　94.31%　and　real-time　detection　at　65　frames　per　second,　which　confirms　the　effectiveness　and　efficiency　of　our　method　in　detecting　camouflaged　people　in　various　typical　desert　and　forest　scenes.　Our　approach　offers　valuable　support　to　improve　the　perception　capabilities　of　unmanned　aerial　vehicles　in　detecting　enemy　forces　and　rescuing　personnel　in　battlefield.　(c)　2023　China　Ordnance　Society.　Publishing　services　by　Elsevier　B.V.　on　behalf　of　KeAi　Communications　Co.　Ltd.　This　is　an　open　access　article　under　the　CC　BY-NC-ND　license　(http://creativecommons.org/　licenses/by-nc-nd/4.0/).