Ship　target　segmentation　in　infrared　scenes　has　always　been　a　hot　topic,　since　it　is　an　important　basis　and　prerequisite　for　infrared-guided　weapons　to　reliably　capture　and　recognize　ship　targets　in　the　sea-level　background.　However,　given　the　small　target　and　fuzzy　boundary　characteristics　of　infrared　ship　images,　obtaining　accurate　pixel-level　labels　for　them　is　hardly　achievable,　which　brings　difficulty　to　train　segmentation　networks.　To　improve　the　segmentation　accuracy　of　infrared　ship　images,　we　propose　a　two-stage　domain　adaptation　method　for　infrared　ship　target　segmentation　(T-DANet),　where　the　segmentation　model　is　trained　using　visible　ship　images　with　clear　target　boundaries.　In　this　case,　the　source　domain　is　the　labeled　visible　ship　images,　while　the　target　domain　is　the　unlabeled　infrared　ship　images.　Specifically,　in　the　first　stage,　we　use　an　image　style　transfer　network　to　convert　the　infrared　ship　images　into　those　with　visible　light　style,　so　that　the　visual　disparity　between　the　two　domain　images　can　be　reduced.　Next,　the　visible,　infrared,　and　converted　infrared　images　are　input　into　the　Deeplab-v2　segmentation　network　for　training,　thereby　obtaining　the　initial　network　weights.　At　this　time,　random　attention　modules　are　added　separately　to　the　low-　and　high-level　spaces　of　Deeplab-v2,　in　order　to　improve　its　feature　extraction　capability.　In　the　second　stage,　we　mix　the　visible　and　infrared　images　through　region　mixing　to　acquire　the　mixed　domain　images,　as　well　as　their　corresponding　labels.　Subsequently,　Deeplab-v2　is　further　trained　using　the　mixed　domain　images　to　attain　better　segmentation　accuracy.　Experimental　results　on　both　the　home-made　visible-infrared　ship　(VI-Ship)　image　dataset　and　the　public　infrared　image　dataset　are　superior　to　those　existing　mainstream　methods,　demonstrating　its　effectiveness.