In　this　paper,　we　studied　infrared　(IR)　maritime　salient　object　detection　based　on　convolutional　neural　networks　(CNNs).　There　are　mainly　two　contributions.　Firstly,　we　constructed　a　large　extended　IR　ship　image　dataset　(ExtIRShip)　for　salient　maritime　target　detection,　including　9,123　labelled　IR　images.　Secondly,　we　proposed　a　global　guided　lightweight　non-local　depth　feature　(DG-Light-NLDF)　model.　We　introduced　Dilated　Linear　Bottleneck　(DLB)　to　replace　the　standard　convolution　and　adding　a　simplified　global　module　to　provide　the　location　information　of　the　potential　salient　object,　the　proposed　method　can　significantly　improve　the　efficiency　of　Light-NLDF.　Experimental　results　demonstrate　that　the　proposed　DG-Light-NLDF　model　could　detect　IR　maritime　salient　objects　more　accurately　with　less　parameters.　In　addition,　comparison　experiments　between　two　datasets　validated　that　the　larger　dataset　is　also　much　more　beneficial　in　improving　saliency　detection　performance.