Object　detection　in　aerial　imagery　captured　by　unmanned　aerial　vehicles　(UAVs)　faces　significant　challenges,　such　as　detecting　small-scale　objects,　handling　variations　in　object　sizes,　and　addressing　limited　computational　resources.　Existing　small-　object　detection　models,　often　large　and　computationally　demanding,　are　unsuitable　for　deployment　on　edge　devices.　To　address　these　limitations,　the　paper　proposes　a　lightweight　model,　MA-YOLOv11s　(multi-attention　YOLOv11s),　which　builds　on　enhancements　to　YOLOv11.　Firstly,　it　introduces　selective　small-object　detection　layers　to　improve　performance　while　managing　computational　complexity.　Secondly,　this　paper　designs　two　lightweight　feature　extraction　modules,　C2SCSA　and　C2MCA,　which　integrate　multiple　attention　mechanisms　to　enhance　feature　extraction　for　small　objects　in　complex　backgrounds　while　minimizing　computational　cost.　Finally,　it　replaces　the　traditional　NMS　method　with　Soft-NMS-SIOU,　significantly　improving　detection　accuracy　and　robustness　in　scenarios　with　densely　overlapping　objects.　In　experiments　on　the　VisDrone2019　dataset,　compared　to　the　YOLOv11s　model,　MA-YOLOv11s　achieves　improvements　of　8.9,　1.3,　10.9,　and　9.7　percentage　points　in　precision,　recall,　mAP50,　and　mAP50:95,　respectively,　with　only　2.291×106　parameters　and　22.4　GFLOPs　of　computation.　The　experimental　results　show　that　the　improved　model　demonstrates　exceptional　small　object　detection　performance　while　maintaining　a　compact　size.　©　2025　Journal　of　Computer　Engineering　and　Applications　Beijing　Co.,　Ltd.;　Science　Press.　All　rights　reserved.