In　the　field　of　object　detection,　the　research　on　the　problem　of　detecting　small　face　is　the　most　extensive,　but　when　there　are　objects　with　obvious　scale　differences　in　the　image,　the　detection　performance　is　not　obvious,　which　is　due　to　the　scale　invariance　properties　of　the　deep　convolutional　neural　networks.　Although　in　recent　years,　there　have　been　some　methods　proposed　to　solve　this　problem　such　as　FPN　and　SNIP,　which　is　based　on　feature　pyramid.　However,　they　have　not　fundamentally　solved　the　problem.　A　regional　cascade　multi-scale　detection　method　has　been　proposed.　First,　a　global　detector　and　several　local　detectors　have　been　trained,　respectively.　The　global　detector　is　trained　by　the　original　training　set,　while　the　local　detector　is　trained　by　the　sub-training　set　generated　by　the　original　training　set.　Second,　the　global　detector　can　detect　object　roughly　and　the　local　detectors　can　produce　more　detailed　results　that　improve　the　performance　of　global　detector.　Finally,　to　integrate　the　detection　results　of　global　detector　and　local　detectors　as　the　output,　non-maximum　suppression　methods　are　used.　The　method　can　be　carried　in　any　depth　model　of　object　detection,　has　good　scalability,　and　is　more　suitable　for　dense　face　detection.　©　The　Institution　of　Engineering　and　Technology　2019.