In　UAV　imagery,　the　intricate　backgrounds　combined　with　the　high　quantity　and　compact　distribution　of　minute　targets　have　consistently　made　target　detection　a　formidable　challenge　in　the　realm　of　computer　vision.　This　study　introduces　an　enhancement　over　the　YOLOv8　algorithm,　wherein　a　sophisticated　multi-scale　convolutional　layer,　integrating　depth-separable　convolution,　attention　mechanisms,　and　multi-scale　processing　techniques,　replaces　the　original　model＇s　convolution.　Moreover,　we　introduce　an　attention　mechanism　for　a　Bi-Level　Routing　within　the　core　component　of　the　base　model,　and　adjustments　are　made　to　the　original　model＇s　loss　function.　Lastly,　to　confirm　the　viability　of　the　enhanced　model　proposed　in　this　paper,　we　conducted　a　validation　of　the　metrics　using　publicly　accessible　datasets.　The　findings　illustrate　that　the　improved　model　outlined　in　this　research　substantially　enhances　target　recognition　accuracy　in　UAV　images.　Furthermore,　the　model　exhibits　superior　performance　in　mitigating　issues　of　duplicate　detection　and　target　omission.　©　2023　IEEE.