With　the　outbreak　of　COVID-19　and　various　influenza　diseases,　it　is　necessary　to　wear　masks　properly　in　crowded　public　places　to　prevent　the　spread　of　the　virus.　Therefore,　detecting　mask-wearing　efficiently　and　accurately　is　essential　for　people’s　physical　health　and　safety.　In　this　paper,　we　present　a　novel　one-stage　mask　detection　method,　named　attention-guided　neural　network　(AGNN)　that　can　efficiently　detect　non-mask-wearing　faces　in　public.　Specifically,　we　started　with　YOLOv5　as　a　baseline　and　integrated　the　coordinate　attention　mechanism　module　into　YOLOv5　to　guide　the　holistic　model　for　improving　the　ability　of　feature　extraction.　Furthermore,　we　explored　utilizing　the　focal　loss　to　solve　the　problem　of　class　imbalance.　The　experiment　is　conducted　on　the　face　mask　detection　dataset　of　real-life　scenes　with　twenty　different　categories.　Experimental　results　demonstrate　that　the　proposed　AGNN　method　achieves　higher　precision　and　recall　than　the　original　YOLOv5　in　multi-classification　mask　detection.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.