Due　to　the　influence　of　the　shooting　angle　of　view　and　the　flight　height,　the　images　taken　by　UAV　often　have　complex　backgrounds　and　contain　a　large　number　of　small　and　unevenly　distributed　objects.　In　order　to　solve　the　problem　that　it　is　difficult　to　accurately　locate　and　recognize　small　objects　in　UAV　images　under　complex　backgrounds,　this　paper　proposes　an　multi-scale　feature　fusion　algorithm　D-A-FS　SSD　(Dilated-Attention-Feature　Fusion　SSD)　based　on　the　combination　of　dilated　convolution　and　attention　mechanism.　In　the　process　of　feature　extraction,　the　receptive　field　of　the　feature　is　expanded　through　the　dilated　convolution,　which　improves　the　network＇s　feature　expression　of　object　distribution　and　scale　difference　information.　And　a　attention　network　is　used　in　our　method　to　effectively　suppresse　the　background　information.　In　the　multi-scale　detection　stage,　our　method　fuses　the　low-level　feature　map　responsible　for　detecting　small　objects　with　the　high-level　feature　map　which　have　much　higher　semantic　information　to　improve　the　recognition　accuracy　of　small　objects.　Experimental　results　show　that　our　method　effectively　improves　the　accuracy　of　UAV　image　object　detection.　©　2020　ACM.