Smoking　is　a　common　behavior　in　daily　life,　but　smoking　in　public　places　not　only　affects　public　health　and　the　health　of　others,　but　also　may　cause　accidents　such　as　fires.　Therefore,　detecting　and　recognizing　smoking　behavior　is　of　great　importance.　Aiming　at　the　problem　that　most　current　target　detection　networks　are　large　in　volume,　have　many　parameters,　and　are　difficult　to　deploy　on　low-performance　device　terminals,　the　article　proposes　a　lightweight　improved　YOLOv5　smoking　behavior　detection　algorithm　model.　This　model　replaces　the　backbone　with　a　lightweight　MobileNetv3　neural　network,　and　at　the　same　time　absorbs　and　references　the　design　ideas　of　ShuffleNet　to　improve　the　width　of　the　neck-head　part,　making　it　smaller　and　more　suitable　for　deployment　on　low-configuration　devices.　The　experimental　data　show　that　after　improvement,　the　new　network　has　80%　less　parameters　and　63%　less　inference　time,　and　when　deployed　on　low-configuration　device　(i3-4000m　2.4ghz　2c4t),　its　inference　time　(50ms)　can　be　reduced　by　half　compared　to　the　original　network　(120ms),　and　meet　the　requirement　of　near　real-time.　Therefore,　the　improved　network　can　reduce　network　parameters　while　ensuring　accuracy,　and　can　achieve　near　real-time　performance　on　low-configuration　devices.　©　2023　SPIE.