The　performance　of　salient　object　detection　has　been　significantly　advanced　by　using　fully　convolutional　networks　(FCN).　However,　it　still　remains　nontrivial　to　take　full　advantage　of　the　multi-level　convolutional　features　for　salient　object　detection.　In　this　paper,　a　dense　grid　network　framework　(denoted　DGrid　)　is　proposed　to　solve　the　above　problem,　which　mainly　consists　of　the　backbone　module,　extended　module　and　fusion　module.　Specifically,　DGrid　utilizes　a　multi-branch　refinement　mechanism　for　saliency　detection.　First,　the　backbone　module　is　used　to　generate　a　coarse　prediction　map.　Then,　the　extended　module,　which　contains　four　branches,　is　used　to　improve　the　resolution　and　precision　of　the　prediction　map　gradually　from　coarse　to　fine.　Moreover,　we　proposed　the　densely　connected　strategy　to　fully　fuse　features　at　different　levels.　Finally,　the　fusion　module　densely　fuses　the　highest　level　features　of　all　branches　to　achieve　the　final　saliency　map.　Experimental　results　on　five　widely　used　benchmark　datasets　demonstrate　that　DGrid　can　improve　the　accuracy　of　detection　by　maintaining　a　high-resolution　feature　branch,　and　it　outperforms　state-of-the-art　approaches　without　any　post-processing.　©　2022,　Springer　Nature　Singapore　Pte　Ltd.