Infrared　ship　target　segmentation　is　the　important　basis　of　infrared　guided　weapon　in　the　sea-air　context.　Typically,　accurate　infrared　ship　target　segmentation　relies　on　a　large　number　of　pixel-level　labels.　However,　it　is　difficult　to　obtain　them.　To　this　end,　we　present　a　method　of　Semi-supervised　Infrared　Ship　Target　Segmentation　with　Dual　Branch　(SeISTS-DB),　which　utilizes　a　small　amount　of　labeled　data　and　a　large　amount　of　unlabeled　data　to　train　model　and　improve　segmentation　performance.　There　are　three　main　contributions.　First,　we　design　a　target　segmentation　branch　to　generate　the　pseudo　labels　for　unlabeled　data.　It　consists　of　a　dual　learning　network　and　a　segmentation　network.　The　dual　learning　network　generates　pseudo　labels　with　weights　for　unlabeled　data.　The　segmentation　network　is　trained　using　both　labeled　data　and　unlabeled　data　with　pseudo　labels　to　achieve　target　segmentation　of　infrared　ship,　obtaining　the　preliminary　segmentation　results.　Secondly,　we　introduce　an　error　segmentation　pixel　correction　branch,　which　contains　a　student　network　and　a　teacher　network,　to　modify　the　pixel　category　error　of　the　preliminary　segmentation　map.　Finally,　the　outputs　of　the　two　branches　are　combined　to　obtain　the　final　segmentation　result.　The　SeISTS-DB　is　compared　with　other　fully　supervised　and　semi-supervised　methods　on　the　infrared　ship　images　dataset.　Experimental　results　demonstrate　that　when　the　labeled　data　accounts　for　1/8　of　the　training　data,　the　mean　Intersection　over　Union　(mIou)　is　respectively　improved　by　15.35%　and　6.19%　at　most.　Besides,　it　is　also　compared　with　other　methods　on　the　public　IRSTD-1k　dataset,　when　the　proportion　of　labeled　images　is　1/8,　the　mIoU　is　respectively　improved　by　11.76%　at　most　compared　to　the　state-of-the-art　semi-supervised　methods,　demonstrating　its　effectiveness.