Polarization　can　improve　the　autonomous　reconnaissance　capability　of　unmanned　aerial　vehicle,　but　it　is　easily　interfered　by　the　variation　of　detection　angle　and　target　materials,　which　affects　the　robustness　of　polarization　detection.　In　this　paper,　a　real-time　low-altitude　camouflaged　target　detection　algorithm　of　YOLO-Polarization　based　on　polarized　images　is　proposed.　The　coded　image　fused　with　multi-polarization　direction　information　is　used　as　input,　the　3D　convolution　module　is　applied　to　extract　the　connection　features　from　the　different　polarization　direction　images,　and　a　feature　enhancement　module　(FEM)　is　introduced　to　further　enhance　the　multi-level　features.　In　addition,　the　cross-level　feature　aggregation　network　is　adopted　to　make　full　use　of　the　feature　information　of　different　scales　to　complete　the　effective　aggregation　of　features,　and　finally　combined　with　multi-channel　feature　information　output　detection　results.　A　dataset　consisting　of　polarized　images　of　low-altitude　camouflaged　targets　(PICO)　which　include　10　types　of　targets　is　constructed.　The　experimental　results　based　on　PICO　dataset　show　that　the　proposed　method　can　effectively　detect　the　camouflaged　targets,　with　mAP0.　5:0.　95　up　to　52.　0%　and　mAP0.　5　up　to　91.　5%　.　The　detection　rate　achieves　55.　0　frames　/　s,　which　meets　the　requirement　of　real-time　detection.　©　2024　China　Ordnance　Industry　Corporation.　All　rights　reserved.