Unmanned　Aerial　Vehicles　(UAV)　have　been　widely　used　in　various　fields,　and　object　detection　has　become　one　of　the　key　technologies　in　the　field　of　UAV　vision.　Considering　the　problems　of　complex　scenes,　variable　scales　and　too　many　small　targets　in　UAV　images,　an　adaptive　aerial　object　detection　algorithm　is　proposed　based　on　multi-scale　convolution.　Firstly,　the　multi-scale　feature　extraction　network　is　constructed,　and　the　multi-scale　convolution　method　is　introduced.　Different　types　of　convolution　are　used　to　check　different　sizes　of　targets　to　expand　the　receptive　field　effectively.　Secondly,　the　convolution　module　is　designed　according　to　the　advantages　of　attention　mechanism,　and　the　feature　weight　is　adaptively　optimized　to　obtain　more　representational　features.　Finally,　a　prediction　network　is　constructed　based　on　multi-scale　feature　fusion.　According　to　the　characteristics　of　small　targets,　multi-level　feature　maps　are　selected　to　fuse　into　high-resolution　feature　maps,　and　object　classification　and　boundary　box　regression　are　carried　out　on　a　single　scale.　Experimental　results　show　that　the　proposed　algorithm　improves　the　object　detection　accuracy　of　UAV　aerial　images,　and　has　good　robustness.　©　2022,　Beihang　University　Aerospace　Knowledge　Press.　All　right　reserved.