Current　object　detection　methods　are　mostly　based　on　the　Faster　R-CNN　which　is　composed　of　two　stages:　1)　the　region　proposal　network　which　roughly　sifts　anchors　and　produces　object　proposals　and　2)　the　detection　network　which　inputs　the　generated　region　proposals　and　makes　predictions　of　category　and　bounding　box　for　each　region.　However,　the　object　proposals　are　always　treated　individually　in　the　detection　network　without　taking　their　relationship　into　consideration.　Specifically,　most　researches　utilize　the　non-maximum　suppression　algorithm　to　reduce　the　number　of　proposals　by　subtracting　the　neighbouring　proposals　for　either　training　or　testing　process.　Though　a　single　proposal　may　be　not　accurate　enough,　a　union　of　its　neighbouring　proposals　and　itself　would　represent　this　object　region　more　comprehensively.　Since　the　neighbouring　relationship　can　be　expressed　by　an　edge　between　two　proposals　(nodes),　the　set　of　region　proposals　can　be　modelled　in　a　graph　structure.　In　this　paper,　we　propose　a　novel　neighbouring　relationship　exploration　model　(NREM)　to　improve　the　object　detection　by　aggregating　the　neighbouring　proposal　graph　(NPG)　based　on　the　graph　convolutional　network　(GCN).　Owing　to　the　effective　exploration　of　complementary　relationship　among　neighbouring　proposals,　our　method　can　improve　the　detection　results　significantly.　Experiments　on　the　PASCAL　VOC　2007　dataset　demonstrate　the　superiority　of　our　proposed　method.　©　2019　IEEE.