In　recent　years,　the　protection　and　management　of　water　environments　have　garnered　heightened　attention　due　to　their　critical　importance.　Detection　of　small　objects　in　unmanned　aerial　vehicle　(UAV)　images　remains　a　persistent　challenge　due　to　the　limited　pixel　values　and　interference　from　background　noise.　To　address　this　challenge,　this　paper　proposes　an　integrated　object　detection　approach　that　utilizes　an　improved　YOLOv5　model　for　real-time　detection　of　small　water　surface　floaters.　The　proposed　improved　YOLOv5　model　effectively　detects　small　objects　by　better　integrating　shallow　and　deep　features　and　addressing　the　issue　of　missed　detections　and,　therefore,　aligns　with　the　characteristics　of　the　water　surface　floater　dataset.　Our　proposed　model　has　demonstrated　significant　improvements　in　detecting　small　water　surface　floaters　when　compared　to　previous　studies.　Specifically,　the　average　precision　(AP),　recall　(R),　and　frames　per　second　(FPS)　of　our　model　achieved　86.3%,　79.4%,　and　92%,　respectively.　Furthermore,　when　compared　to　the　original　YOLOv5　model,　our　model　exhibits　a　notable　increase　in　both　AP　and　R,　with　improvements　of　5%　and　6.1%,　respectively.　As　such,　the　proposed　improved　YOLOv5　model　is　well-suited　for　the　real-time　detection　of　small　objects　on　the　water＇s　surface.　Therefore,　this　method　will　be　essential　for　large-scale,　high-precision,　and　intelligent　water　surface　floater　monitoring.