In　recent　years,　human　detection　in　indoor　scenes　has　been　widely　applied　in　smart　buildings　and　smart　security,　but　many　related　challenges　can　still　be　difficult　to　address,　such　as　frequent　occlusion,　low　illumination　and　multiple　poses.　This　paper　proposes　an　asymmetric　adaptive　fusion　two-stream　network　(AAFTS-net)　for　RGB-D　human　detection.　This　network　can　fully　extract　person-specific　depth　features　and　RGB　features　while　reducing　the　typical　complexity　of　a　two-stream　network.　A　depth　feature　pyramid　is　constructed　by　combining　contextual　information,　with　the　motivation　of　combining　multiscale　depth　features　to　improve　the　adaptability　for　targets　of　different　sizes.　An　adaptive　channel　weighting　(ACW)　module　weights　the　RGB-D　feature　channels　to　achieve　efficient　feature　selection　and　information　complementation.　This　paper　also　introduces　a　novel　RGB-D　dataset　for　human　detection　called　RGBD-human,　on　which　we　verify　the　performance　of　the　proposed　algorithm.　The　experimental　results　show　that　AAFTS-net　outperforms　existing　state-of-the-art　methods　and　can　maintain　stable　performance　under　conditions　of　frequent　occlusion,　low　illumination　and　multiple　poses.