In　recent　years,　the　rapid　development　of　the　unmanned　aerial　vehicle　(UAV)　technology　has　generated　a　large　number　of　aerial　photography　images　captured　by　UAV.　Consequently,　the　object　detection　in　UAV　aerial　images　has　emerged　as　a　recent　research　focus.　However,　due　to　the　flexible　flight　heights　and　diverse　shooting　angles　of　UAV,　two　significant　challenges　have　arisen　in　UAV　aerial　images:　extreme　variation　in　target　scale　and　the　presence　of　numerous　small　targets.　To　address　these　challenges,　this　article　introduces　a　semantic　information-guided　fusion　module　specifically　tailored　for　small　targets.　This　module　utilizes　high-level　semantic　information　to　guide　and　align　the　underlying　texture　information,　thereby　enhancing　the　semantic　representation　of　small　targets　at　the　feature　level　and　subsequently　improving　the　model＇s　ability　to　detect　them.　In　addition,　this　article　introduces　a　novel　global-local　fusion　detection　strategy　to　strengthen　the　detection　of　small　targets.　We　have　redesigned　the　foreground　region　assembly　method　to　address　the　drawbacks　of　previous　methods　that　involved　multiple　inferences.　Extensive　experiments　conducted　on　the　VisDrone　and　UAVDT　datasets　demonstrate　that　our　two　self-designed　modules　can　significantly　enhance　the　detection　capability　of　small　targets　compared　with　the　YOLOX-M　model.　Our　code　is　publicly　available　at:　https://github.com/LearnYZZ/GLSDet.