As　the　city　expands,　the　river　shoreline　is　constantly　being　encroached　upon,　and　the　river＇s　flow　capacity　and　water　environment　are　damaged　severely.　Therefore,　efficient　methods　for　monitoring　complicated　river　courses　are　urgently　needed.　This　paper　precents　a　new　method　for　collecting　images　and　data　of　a　river　and　its　banks　using　drones　and　enhancing　data　with　the　Generative　Adversarial　Network.　We　identify　and　locate　five　kinds　of　typical　foreign　bodies　on　the　river,　based　on　the　YOLOv5　algorithm　and　the　coordinate　transformation　localization　algorithm.　The　target　recognition　algorithm　of　this　model　introduces　the　attention　mechanism　into　the　backbone　network　and　uses　EIOU-Focal　Loss　as　its　loss　function　to　improve　YOLOv5　in　detection　accuracy　and　convergence　speed.　The　results　show　that　data　enhancement　improves　the　model’s　target　recognition　and　raises　the　mean　Average　Precision　(mAP)　by　9.9%.　The　ablation　experiment　results　verify　that　this　model　has　the　highest　detection　accuracy,　with　its　maximum　mAP　of　0.96　or　an　increase　of　11.6%　relative　to　the　one　before　improvement.　Its　positioning　results　show　the　real　average　error　of　the　algorithm　target　object　is　not　greater　than　3m,　which　means　a　high　accuracy.　Application　of　the　improved　model　to　the　Minjiang　River　section　in　Fujian　has　verified　its　higher　accuracy　in　detecting　target　objects　and　its　significance　for　related　research.　©　2025　Tsinghua　University.　All　rights　reserved.