The　saliency　object　detection　is　a　hot　topic　of　computer　vision.　Traditional　saliency　detection　methods　are　overly　dependent　on　handcrafted　low-level　features.　The　saliency　detection　methods　based　on　deep　learning　can　effectively　solve　the　problem,　which　extracts　high-level　features　automatically.　However,　there　are　some　noises　in　the　extracted　high-level　features　that　affect　the　detection　performance.　We　propose　a　deep　learning　framework　for　saliency　detection　based　on　global　prior　and　local　context.　First,　we　use　feature　maps　generated　by　combining　some　middle-level　features　as　the　input　of　global-prior-based　deep　learning　model,　which　can　reduce　the　interference　of　distracting　feature　information　for　the　saliency　detection.　Then,　two　deep　learning　models　use　respectively　local　contexts　of　color　image　and　depth　map　as　input,　which　combine　global　prior　to　generate　the　initial　saliency　map.　Finally,　the　optimized　saliency　map　can　be　obtained　based　on　spatial　consistence　and　appearance　similarity.　Experiments　on　two　publicly　available　datasets　show　that　the　proposed　method　performs　better　than　other　nine　state-of-the-art　approaches.　(C)　2018　SPIE　and　IS&T