With　the　rapid　development　of　deep　learning,　UAV　target　detection　technology　based　on　computer　vision　and　artificial　intelligence　has　been　widely　used　in　practice.　However,　due　to　the　instability　of　UAV　movement,　limited　by　load　and　endurance,　the　development　of　UAV　target　detection　is　slow,　and　there　are　challenges　such　as　significant　changes　in　target　scale,　occlusion　between　objects,　and　changes　in　target　density.　This　paper　builds　on　the　network　model　structure　of　YOLOv5　to　address　these　challenges.　It　adds　a　detection　head　generated　from　low-level　feature　layers　and　high-resolution　combined　feature　maps　to　detect　tiny　objects.　We　utilize　the　Bifpn　network　structure　and　a　weighted　fusion　splicing　approach　to　fuse　more　features　and　introduce　an　improved　Coordinate　Attention　to　obtain　location　information　for　feature　enhancement　accurately.　Extensive　experiments　on　the　Visdrone2021　dataset　show　that　the　model　achieves　good　results　in　UAV　target　detection　and　is　helpful　for　tiny　and　occluded　target　detection.　©　2023　SPIE.