In　the　dynamic　landscape　of　saliency　detection,　convolutional　neural　networks　have　emerged　as　catalysts　for　innovation,　but　remain　largely　tailored　for　RGB　imagery,　falling　short　in　the　context　of　infrared　images,　particularly　in　memory-restricted　environments.　These　existing　approaches　tend　to　overlook　the　wealth　of　contour　information　vital　for　a　nuanced　analysis　of　infrared　images.　Addressing　this　notable　gap,　we　introduce　the　novel　Two-branch　Edge　Guided　Lightweight　Network　(TBENet),　designed　explicitly　for　the　robust　analysis　of　infrared　image　saliency　detection.　The　main　contributions　of　this　paper　are　as　follows.　First,　we　formulate　the　saliency　detection　task　as　two　subtasks,　contour　enhancement　and　foreground　segmentation.　Therefore,　the　TBENet　is　divided　into　two　specialized　branches:　a　contour　prediction　branch　for　extracting　target　contour　and　a　saliency　map　generation　branch　for　separating　the　foreground　from　the　background.　The　first　branch　employs　an　encoder-decoder　architecture　to　meticulously　delineate　object　contours,　serving　as　a　guiding　blueprint　for　the　second　branch.　This　latter　segment　adeptly　integrates　spatial　and　semantic　data,　creating　a　precise　saliency　map　that　is　refined　further　by　an　innovative　edge-weighted　contour　loss　function.　Second,　to　enhance　feature　integration　capabilities,　we　propose　depthwise　multi-scale　and　multi-cue　modules,　facilitating　sophisticated　feature　aggregation.　Third,　a　high-level　linear　bottleneck　module　is　devised　to　ensure　the　extraction　of　rich　semantic　information,　and　by　replacing　the　standard　convolution　with　the　depthwise　convolution,　it　is　beneficial　to　reduce　model　complexity.　Additional,　we　reduce　the　number　of　channels　of　the　feature　maps　from　each　stage　of　the　decoder　to　further　enhance　the　lightweight　of　the　model.　Last,　we　construct　a　novel　infrared　ship　dataset　Small-IRShip　to　train　and　evaluate　our　proposed　model.　Experimental　results　on　the　homemade　dataset　Small-IRShip　and　two　publicly　available　datasets,　namely　RGB-T　and　IRSTD-1k,　demonstrate　TBENet＇s　superior　performance　over　state-of-the-art　methods,　affirming　its　effectiveness　in　harnessing　edge　information　and　incorporating　advanced　feature　integration　strategies.