The　traditional　visual　attention　mechanism　is　complex　and　rough-detection　for　visual　saliency　detection　indoor　red-green-blue　(RGB)　image.　In　order　to　overcome　these　defects,　a　new　fast　visual　saliency　object　detection　method　based　on　fusion　depth　information　on　indoor　RGB　image　is　proposed.　A　certain　scale　image　is　obtained　by　sub-sampling　and　pyramid-quantization　to　reduce　the　spatial　resolution　of　the　images　so　as　to　decrease　the　computational　complexity.　The　intensity,　red-green　and　yellow-blue　three-channel　features　visual　attention　mechanism　significant　detection　model　is　proposed　to　acquire　saliency　map.　The　saliency　growing　strategy　is　proposed　to　acquire　the　precise　saliency　region　in　the　saliency　analysis.　The　fusion　depth　information　is　utilized　to　detect　the　objects　in　salient　region.　The　feasibility　and　effectiveness　of　the　algorithm　is　verified　in　indoor　detection　experiments.　©,　2014,　Science　Press.　All　right　reserved.