In　recent　years,　UAV　(Unmanned　Aerial　Vehicle)　has　developed　rapidly　and　has　been　widely　used　in　various　fields,　which　has　also　posed　a　great　threat　to　the　security　of　military　bases,　government　departments　and　other　sensitive　areas.　Considering　the　problem　of　low　accuracy　and　insufficient　datasets　of　current　algorithm　in　the　target　detection　of　＇low,　and　slow　small＇　UAV,　and　in　order　to　improve　the　real-time　detection　performance　of　＇low,　and　slow　small＇　UAV,　this　paper　proposes　an　improvement　strategy　of　anchor,　and　obtains　the　anchor　size　suitable　for　UAV　dataset　through　K-means++　clustering　algorithm,　making　the　size　of　anchor　more　targeted.　Meanwhile,　the　different　loss　functions　and　IoU　related　threshold　parameters　for　research,　analysis　of　different　thresholds,　and　the　influence　of　different　loss　functions　for　UAV　detection　precision,　thus　by　selecting　the　best　loss　function　and　the　threshold　is　set　to　YOLOv4　target　detection　algorithm　is　improved,　further,　enhancing　the　YOLOv4　for　＇low,　and　slow　small＇　UAV　detection　accuracy.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.