Image　captioning　technology　has　become　an　important　solution　for　intelligent　robots　to　understand　image　content.　How　to　extract　image　information　effectively　is　the　key　to　generate　accurate　and　reliable　captions.　In　this　paper,　we　propose　a　dual　self-attention　based　network　(DSAN)　for　image　captioning.　Specifically,　we　design　a　Dual　Self-Attention　Module　(DSAM)　embedded　into　an　encoding-decoding　architecture　to　capture　the　contextual　information　in　the　image,　which　can　adaptively　integrate　local　features　with　global　dependencies.　The　DSAM　can　significantly　improve　the　caption　results　by　modeling　rich　contextual　dependencies　over　local　features.　Experimental　results　on　the　MS　COCO　dataset　show　that　the　proposed　DSAN　can　achieve　better　performance　than　existing　methods.