Because　the　image　of　UAV　aerial　photography　is　easy　to　be　affected　by　light,　sea　area　and　other　conditions,　there　are　many　kinds　of　ships.　Under　different　conditions,　the　characteristics　of　ships　are　different,　which　makes　the　target　recognition　more　difficult.　In　order　to　improve　the　efficiency　of　sea　surface　supervision　and　make　the　sea　surface　management　more　intelligent,　an　ocean　ship　detection　algorithm　based　on　aerial　photography　image　is　proposed.　In　this　paper,　the　improved　Yolo　algorithm　is　mainly　used　for　high-efficiency　ship　detection　of　aerial　video,　which　can　achieve　real-time　performance　and　detection　speed　of　23fps.　In　order　to　improve　the　accuracy,　this　paper　proposes　a　standardized　mechanism　of　fixed　frame　length　detection　results,　which　uses　deep　learning　mask　RCNN　algorithm　for　fine　detection　of　specific　frame　images,　and　the　detection　map　is　85%,　which　improves　the　detection　speed　without　affecting　the　detection　speed　The　accuracy　of　the　algorithm　forms　an　efficient　and　accurate　algorithm　for　the　detection　of　ships　on　the　sea,　which　brings　convenience　to　the　management　of　the　sea.　©　2020　IEEE.