Performance　of　high　resolution　image　process　is　one　of　the　kernel　problems　that　must　be　addressed　to　promote　the　development　of　embedded　system.　In　this　study,　a　scalable　bi-level　parallel　object　detection　framework　based　on　heterogeneous　manycore　cluster　was　established　to　improve　object　detection　performance　for　embedded　device.　First,　the　fundamental　principle　of　local　binary　pattern　and　cascade　classifier　combined　object　detection　method　was　introduced　as　the　basis　of　the　research.　Second,　a　set　of　key　algorithm　design　to　parallel　access　and　process　image　for　object　detection　based　on　Parallella　manycore　platform　was　proposed　to　improve　the　detection　speed　and　the　computational　resource　efficiency　on　single　node.　Third,　a　Message　Passing　Interface　based　distributed　framework　was　established　for　cluster　environment　to　further　improve　the　performance.　Finally,　an　experiment　of　face　detection　application　was　conducted　to　evaluate　the　accuracy　and　performance　of　this　framework.　The　experimental　results　show　that　on　one　node,　the　proposed　object　detection　system　provides　7.8　times　speedup　than　a　serial　algorithm　on　dual-core　ARM　which　was　integrated　in　Parallella　with　similar　accuracy,　and　in　cluster　environment,　the　performance　will　be　doubled.　The　results　demonstrate　the　promising　application　of　the　proposed　framework　in　the　field　of　object　detection　performance　improvement.