The　fusion　of　spectral-polarimetric　information　can　improve　the　autonomous　reconnaissance　capability　of　unmanned　aerial　vehicles　(UAVs)　in　detecting　artificial　targets.　However,　the　current　spectral　and　polarization　imaging　systems　typically　suffer　from　low　image　sampling　resolution,　which　can　lead　to　the　loss　of　target　information.　Most　existing　segmentation　algorithms　neglect　the　similarities　and　differences　between　multimodal　features,　resulting　in　reduced　accuracy　and　robustness　of　the　algorithms.　To　address　these　challenges,　a　real-time　spectral-polarimetric　segmentation　algorithm　for　artificial　targets　based　on　an　efficient　attention　fusion　network,　called　ESPFNet　(efficient　spectral-polarimetric　fusion　network)　is　proposed.　The　network　employs　a　coordination　attention　bimodal　fusion　(CABF)　module　and　a　complex　atrous　spatial　pyramid　pooling　(CASPP)　module　to　fuse　and　enhance　low-level　and　high-level　features　at　different　scales　from　the　spectral　feature　images　and　the　polarization　encoded　images,　effectively　achieving　the　segmentation　of　artificial　targets.　Additionally,　the　introduction　of　the　residual　dense　block　(RDB)　module　refines　feature　extraction,　further　enhancing　the　network＇s　ability　to　classify　pixels.　In　order　to　test　the　algorithm＇s　performance,　a　spectral-polarimetric　image　dataset　of　artificial　targets,　named　SPIAO　(spectral-polarimetric　image　of　artificial　objects)　is　constructed,　which　contains　various　camouflaged　nets　and　camouflaged　plates　with　different　properties.　The　experimental　results　on　the　SPIAO　dataset　demonstrate　that　the　proposed　method　accurately　detects　the　artificial　targets,　achieving　a　mean　intersection-over-union　(MIoU)　of　80.4%,　a　mean　pixel　accuracy　(MPA)　of　88.1%,　and　a　detection　rate　of　27.5　frames　per　second,　meeting　the　real-time　requirement.　The　research　has　the　potential　to　provide　a　new　multimodal　detection　technique　for　enabling　autonomous　reconnaissance　by　UAVs　in　complex　scenes.