The　performance　of　current　pre-trained　models　for　unmanned　aerial　vehicle　(UAV)　detection　is　directly　impacted　by　dataset　quality,　and　the　manual　ingestion　and　labeling　required　for　dataset　updates　are　both　time　and　labor　consuming　We　present　an　automated　procedure　to　constructing　UAV　synthetic　datasets　utilizing　31)　photorealistic　graphics.　By　implementing　a　targeted　training　strategy,　we　significantly　enhance　the　performance　of　object　detection　algorithms　under　similar　conditions.　Experimental　results　demonstrate　that,　compared　to　a　baseline　training　model,　our　model,　trained　with　only　100　data　samples,　achieves　a　64.9%　increase　in　the　mAP@0.95　metric　relative　to　the　original　model.　Moreover,　with　5,000　data　samples,　our　model　outperforms　the　performance　achieved　by　the　traditional　model　trained　on　10,000　samples.　This　reduction　in　the　requirement　for　real　drone　data　samples　streamlines　the　training　process　for　UAV　detection　models.