Based　on　deep　learning,　an　underwater　image　instance　segmentation　method　is　proposed.　Firstly,　in　view　of　the　scarcity　of　underwater　related　data　sets,　the　size　of　the　data　set　is　expanded　by　measures　including　image　rotation　and　flipping,　and　image　generation　by　a　generative　adversarial　network　(GAN).　Next,　the　underwater　image　data　set　is　finally　constructed　by　manual　labeling.　Then,　in　order　to　solve　the　problems　of　color　shift,　blur　and　the　poor　contrast　of　optical　images　caused　by　the　complex　underwater　environment　and　the　attenuation　and　scattering　of　light,　an　underwater　image　enhancement　algorithm　is　used　to　first　preprocess　the　data　set,　and　several　algorithms　are　discussed,　including　multi-scale　Retinex　(MSRCR)　with　color　recovery,　integrated　color　model　(ICM),　relative　global　histogram　stretching　(RGHS)　and　unsupervised　color　correction　(UCM),　as　well　as　the　color　shift　removal　proposed　in　this　work.　Specifically,　the　results　indicate　that　the　proposed　method　can　largely　increase　the　segmentation　mAP　(mean　average　precision)　by　85.7%　compared　with　without　the　pretreatment　method.　In　addition,　based　on　the　characteristics　of　the　constructed　underwater　dataset,　the　feature　pyramid　network　(FPN)　is　improved　to　some　extent,　and　the　preprocessing　method　is　further　combined　with　the　improved　network　for　experiments　and　compared　with　other　neural　networks　to　verify　the　effectiveness　of　the　proposed　method,　thus　achieving　the　effect　and　purpose　of　improving　underwater　image　instance　segmentation　and　target　recognition.　The　experimental　analysis　results　show　that　the　proposed　model　can　achieve　a　mAP　of　0.245,　which　is　about　1.1　times　higher　than　other　target　recognition　models.　©　2024　by　the　authors.