Unmanned　aerial　vehicle　(UAV)　remote　sensing　detection　plays　an　important　role　in　military　reconnaissance,and　the　polarization　detection　is　to　utilize　the　polarization　changes　generated　by　the　interaction　between　polarized　light　and　target　to　improve　the　target　contrast.　However,in　complex　scenes,　the　small　targets　are　less　distinguishable　from　the　background　due　to　their　similar　features　and　the　insufficient　spatial　information,resulting　in　difficulties　in　detection.　To　this　end,a　polarization　camouflaged　small　object　detection　(PCSOD)-YOLO　algorithm　is　proposed,and　an　efficient　layer　attention　module-coordinated　attention　(ELAM-CA)　and　a　spatial　pyramid　pooling　cross　stage　partial　channel-3D　weights　attention　(SPPCSPC-3DWA)　module　are　designed　to　capture　the　polarization　features　and　semantic　information　of　target,enhancing　the　ability　to　understand　the　contextual　information.　A　dynamic　small　target　detection　head　is　designed　to　enhance　the　ability　to　extract　the　features　of　small　targets　through　dynamic　convolution,and　the　detected　results　of　small　target　are　outputted　using　the　feature　information　from　different　scales　and　the　multi-channel　feature　information.　A　polarization　image　of　camouflaged　small　objects　(PICSO)　dataset　is　constructed　for　the　camouflaged　small　target　polarization　images.　Experiments　on　the　PICSO　dataset　show　that　the　proposed　method　can　effectively　detect　the　camouflaged　small　targets,　with　mAP0.　5　and　mAP0.　5:0.　95　reaching　92.　4%　and　47.　8%,respectively.　The　detection　rate　reaches　60.　6　frames　per　second,meeting　the　real-time　requirements.　©　2025　China　Ordnance　Industry　Corporation.　All　rights　reserved.