Unmanned　Aerial　Vehicle　(UAV)　detection　is　important　to　limit　illegal　UAV　activity.　The　object　detection　method　based　on　computer　vision　has　the　advantages　of　low　cost　and　strong　discrimination.　Due　to　the　small　size　of　UAV,　existing　object　detection　methods　are　still　insufficient　in　the　detection　performance　of　UAV.　In　this　paper,　we　make　targeted　improvements　based　on　the　powerful　YOLO　v5　to　improve　the　detection　performance　of　small　objects.　First,　we　take　the　divide　and　conquer　approach,　the　high-resolution　image　is　cut　into　multiple　image　blocks　to　be　detected　separately,　and　the　pure　sky　region　without　object　is　removed　through　the　sky　region　recognition　algorithm.　In　terms　of　network　structure,　to　utilize　the　spatial　information　in　the　shallow　layers,　the　neck　and　detection　head　of　YOLO　v5　are　shifted　to　the　shallower　layer　of　the　backbone.　Meanwhile,　Image　Pyramid　Guidance　Network　is　also　introduced　into　the　network　structure　of　YOLO　v5.　Experiments　show　that　the　proposed　approach　achieved　Recall　of　0.881　and　mAP0.5　of　0.83　for　small　UAVs　in　our　data　set,　significant　improvement　compared　to　before　improvement.　©　VDE　VERLAG　GMBH　-　Berlin　-　Offenbach.