With　the　continuous　development　of　computer　technology　and　deep　learning,　object　detection　technology　has　received　great　attention　in　recent　years.　Especially　on　the　problem　of　3D　object　detection　in　AR　field,　a　variety　of　novel　algorithms　are　in　bloom.　However,　these　algorithms　are　either　low　precision　or　slow　speed.　Therefore,　we　propose　an　object　detection　and　pose　estimation　method　for　AR　Application,　which　is　a　model　for　6D　pose　estimation　of　3D　object　based　on　2D　object　detection.　In　our　model,　we　firstly　input　the　2D　projection　image　of　the　3D　model　into　the　improved　RetinaNet　model,　and　detect　the　coordinates　of　the　vertex　and　center　point　of　the　object　on　the　image.　Then　the　coordinates　on　the　image　and　the　coordinates　on　the　CAD　are　matched　by　point　matching.　Then,　the　EPnP　improved　by　Perspective-n-Point　(PnP)　algorithm　is　used　to　complete　the　pose　estimation　of　3D　object.　Finally,　we　test　the　performance　of　our　algorithm.　The　results　show　that　our　accuracy　has　reached　the　average　level　of　the　mainstream　algorithms.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.