Mobile　phones　are　becoming　increasingly　prevalent　in　modern　life,　and　software　testing　is　a　very　important　aspect　of　ensuring　the　quality　of　the　software.　Game　Apps　(Applications)　on　mobile　devices　have　unique　characteristics,　including　frequent　game　version　updates　and　more　complex　UI　(User　Interface)　elements　than　ordinary　APP　page　elements.　The　identification　of　interface　elements　is　the　first　step　in　automated　testing　of　game　apps.　However,　the　existing　target　detection　algorithm　may　ignore　some　small　target　elements　for　the　recognition　of　game　GUI　(Graphical　User　Interface)　elements,　resulting　in　low　recognition　accuracy.　This　paper　aims　at　the　automatic　testing　of　mobile　games　and　proposes　a　new　model　named　YOLOv5-ABLN　suitable　for　game　APP　GUI　element　recognition.　The　model　is　based　on　the　YOLOv5s　model,　and　the　BRA　(Bi-Level　Routing　Attention)　module　is　introduced　in　the　YOLOv5s　Head,　and　the　NWD　(Normalized　Wasserstein　Distance)　is　introduced　in　the　loss　function　calculation　to　form　a　new　measurement　index.　To　improve　the　model＇s　ability　to　recognize　clickable　elements　of　the　game　interface,　a　dataset　of　2,400　images　is　created　and　divided　into　training　and　validation　sets　in　an　8:2　ratio.　Furthermore,　a　test　set　consisting　of　298　UI　interfaces　from　six　actively　popular　games　is　collected　for　evaluation　and　experimented　on　YOLOv5　and　the　improved　YOLOv5-ABLN　model　respectively.　The　results　show　that　YOLOv5　achieves　a　mAP　(mean　Accuracy　Precision)　of　90.2%　in　the　task　of　detecting　GUI　elements　of　game　applications.　The　improved　YOLOv5-ABLN　model　achieves　a　mAP　of　91.9%　in　the　same　task,　which　is　a　1.7%　improvement　over　the　original　model.　©　2023　IEEE.