In　order　to　solve　the　problems　of　low　detection　accuracy,　false　detection　and　high　miss　detection　rate　of　small　targets　in　target　detection　tasks,　this　paper　is　a　multi-target　detection　method　based　on　YOLOv4　convolutional　neural　network.　The　proposed　method　is　based　on　YOLOv4.　The　semantic　information　of　high-level　features　is　first　propagated　to　the　low-level　network　through　FPN　sampling,　and　then　it　is　fused　with　the　high-resolution　information　of　the　underlying　features　to　improve　the　detection　effect　of　small　target　detection　objects.　The　information　transmission　path　from　the　bottom　to　the　top　is　enhanced　by　downsampling　the　feature　pyramid,　and　finally　the　feature　maps　of　different　layers　are　fused　to　achieve　relevant　predictions.　Experiments　prove　that　the　method　proposed　in　this　paper　has　good　results.　©　Published　under　licence　by　IOP　Publishing　Ltd.