In　the　aerial　image　of　unmanned　aerial　vehicle(UAV),　the　target　is　usually　small,　and　the　shooting　angle　and　height　are　variable.　To　address　the　problems,　we　proposed　an　adaptive　drone　object　detection　algorithm　based　on　the　multi-scale　feature　fusion.　First,　lightweight　feature　extraction　network　was　established　using　the　advantages　of　deep　separable　convolution　and　residual　learning.　Second,　a　multi-scale　adaptive　candidate　region　generation　network　was　constructed,　and　feature　maps　with　the　same　spatial　size　were　weighted　and　merged　based　on　the　channel　dimensions,　which　enhance　the　feature　expression　ability　to　objects.　Based　on　these　multi-scale　featured　maps,　the　use　of　semantic　features　to　generate　target　candidate　frames　can　be　more　matchable　with　real　objects.　Moreover,　simulation　experiments　demonstrate　that　this　algorithm　can　effectively　improve　the　accuracy　of　UAV　detection　and　have　better　robustness.　©　2020,　Chinese　Lasers　Press.　All　right　reserved.