The　traditional　object　detection　algorithm　can　only　obtain　the　horizontal　detection　frame.　In　this　paper,　to　solve　the　problem　of　large　background　noise　and　missing　angle　information　in　the　detection　frame,　a　bottle　target　detection　model　based　on　SSD　(Single　Shot　multi-frame　Detector)　algorithm　is　proposed.　The　convolutional　neural　network　is　used　as　the　feature　extraction　unit,　which　realized　the　gradient　bounding　box　regression　by　introducing　angle　prediction.　In　order　to　improve　the　accuracy　of　the　model,　the　feature　pyramid　structure　is　introduced　into　the　feature　extraction　network,　feature　pyramid　structure　enables　deep　semantic　information　in　the　network　to　be　combined　with　shallow　layers.　The　prior　box　is　determined　by　K-means　clustering　algorithm,　it　is　used　to　make　the　network　model　more　suitable　for　the　bottle　target.　The　experimental　results　show　that　the　proposed　algorithm　is　better　than　the　original　SSD　algorithm,　the　AP　value　reaches　90.7%,　and　the　real-time　performance　of　the　algorithm　is　also　greatly　improved.　In　summary,　the　position　+　size　+　angle　simultaneous　detection　is　more　suitable　for　the　target　pose　recognition　application,　such　as　grabbing　the　bottle　in　garbage　sorting.　©　2019　IEEE.