In　today＇s　society,　intelligent　video　surveillance　plays　an　important　role　in　social　security,　traffic　scheduling,　national　security　and　other　fields.　One　of　the　research　hotspots　is　people　statistics　based　on　image　processing,　which　has　strategic　significance　in　practical　applications.　Aimed　at　the　problem　that　the　low　accuracy　in　the　actual　application　scenario,　the　limited　hardware　resources,　and　the　low　operation　efficiency,　this　paper　proposes　a　multi-feature　target　detection　model　based　on　the　lightweight　deep　learning　network　MobileNet　[1],　which　can　be　used　in　intelligent　terminals.　The　basic　feature-extraction　network　MobileNet　as　a　lightweight　network　can　provide　a　flexible　alternative　configuration　in　terms　of　efficiency　and　accuracy.　The　underlying　detection　network　selects　a　single　deep　nerual　network,　named　SSD　[2].　The　algorithm　can　achieve　multi-scale　target　detection,　and　uses　the　target　position　and　category　to　perform　one-time　regression.　In　this　paper,　the　activation　function　of　SSD　is　changed　into　SeLU　(scaled　exponential　linear　units)　[3],　which　improves　the　robustness　of　the　algorithm.　At　the　same　time,　the　work　of　sample　diversity　and　data　enhancement　has　been　made,　and　the　characteristics　of　the　human　body　above　the　shoulders　have　been　fully　utilized.　Experiments　have　shown　that　the　improved　network　structure　based　on　MobileNet　has　higher　detection　accuracy,　lower　delay,　excellent　robustness,　while　the　number　of　model　parameters　is　effectively　reduced.　©　2019　IOP　Publishing　Ltd.　All　rights　reserved.