To　address　the　challenges　of　significant　changes　in　the　field　of　view　and　complex　spatiotemporal　information　in　unmanned　aerial　vehicle　aerial　image　target　detection,　a　model　for　small　object　detection　in　aerial　photography　based　on　low　dimensional　image　feature　fusion　is　presented　grounded　on　the　YOLOv5(you　only　look　once　version　5)　architecture.　Coordinate　attention　is　introduced　to　improve　the　inverted　residuals　of　MobileNetV3,　thereby　increasing　the　spatial　dimension　information　of　images　while　reducing　parameters　of　the　model.　The　YOLOv5　feature　pyramid　network　structure　is　improved　to　incorporate　feature　images　from　shallow　networks.　The　ability　of　the　model　to　represent　low-dimensional　effective　information　of　images　is　enhanced,　and　consequently　the　detection　accuracy　of　the　proposed　model　for　small　objects　is　improved.　To　reduce　the　impact　of　complex　background　in　the　image,　the　parameter-free　average　attention　module　is　introduced　to　focus　on　both　spatial　attention　and　channel　attention.　VariFocal　Loss　is　adopted　to　reduce　the　weight　proportion　of　negative　samples　in　the　training　process.　Experiments　on　VisDrone　dataset　demonstrate　the　effectiveness　of　the　proposed　model.　The　detection　accuracy　is　effectively　improved　while　the　model　complexity　is　significantly　reduced.　©　2024　Science　Press.　All　rights　reserved.