In　application　scenarios　such　as　UAV　inspection,　deep　learning-based　object　detection　methods　are　increasingly　used　to　improve　the　automation　of　line　inspection.　In　the　aerial　view　scene,　the　drone　is　usually　fly　at　a　high　altitude　from　the　ground,　so　the　proportion　of　the　object　in　the　image　is　relatively　small.　When　the　YoloV3　network　identifies　small　objects,　the　detection　result　would　not　be　good　because　there　is　less　information　in　the　8x　downsampling　feature　map.　In　this　paper,　base　on　the　LaSOT　data　set,　the　YoloV3　network　has　been　modified　by　adjusting　the　values　of　anchors　and　establishing　the　4x　downsampling　prediction　layer　to　enhance　the　detection　effect　of　small　objects.　Compared　with　the　original　YoloV3　network,　the　improved　YoloV3　network　has　a　certain　improvement　in　convergence　ability　and　detection　accuracy　compared　to　the　original　YoloV3　network.　©　2020　IEEE.