With　the　rapid　development　of　artificial　intelligence　science,　more　and　more　researchers　try　to　use　deep　learning　to　train　neural　networks　and　have　achieved　great　success　in　object　detection.　Vehicle　detection　based　on　UAV　image　is　a　special　field　of　object　detection.　Due　to　the　low　resolution　of　the　vehicle　object,　complex　background,　and　less　image　information,　it　is　challenging　to　extract　robust　visual　and　spatial　features　from　the　depth　network　and　accurately　locate　the　object　in　complex　scenes.　In　this　paper,　combining　the　characteristics　of　vehicles　in　aerial　images,　we　design　a　novel　feature　pyramid　network　called　channel-spatial　attention　fused　feature　pyramid　network　(CSF-FPN)　with　Faster　R-CNN　as　the　basic　framework.　In　CSF-FPN,　a　hybrid　attention　mechanism　and　feature　cross-fusion　module　are　introduced,　so　that　feature　maps　can　be　generated　with　enhanced　spatial　and　channel　interdependence　to　extract　richer　semantic　information.　After　our　CSF-FPN　is　integrated　into　the　Faster　R-CNN　network,　the　detection　performance　of　small　objects　is　greatly　improved.　The　experimental　results　based　on　the　VEDIA　Dataset　showed　that　the　proposed　framework　could　effectively　detect　the　vehicle　in　large　scene　azimuth.　Compared　with　the　existing　advanced　methods,　mAP　and　F1-score　are　improved.　©　2021　ACM.