At　present,　underwater　exploration　and　salvage,　underwater　archaeology,　and　other　underwater　operations　still　mainly　rely　on　professional　underwater　operators.　Considering　that　artificial　underwater　operation　is　faced　with　the　problems　of　small　exploration　scope,　poor　working　environment,　and　low　work　efficiency,　it　is　the　future　trend　to　use　robots　to　replace　manual　underwater　operation　in　related　fields.　Most　of　the　current　underwater　robots　are　artificial　remote-controlled,　which　lack　intelligent　detection　and　autonomous　grasping　system.　In　this　paper,　a　grasping　robot　equipped　with　an　AI　computing　platform　is　developed　to　enable　the　autonomous　grasping　of　underwater　targets　by　using　stereo　vision　technology.　For　the　problem　of　difficult　detection　due　to　the　small　size　and　occlusion　of　underwater　targets,　this　paper　proposes　Cascade　DetNet,　which　can　improve　recognition　accuracy.　The　experimental　results　show　that　our　proposed　method　achieves　the　best　performance　on　URPC　dataset　compared　with　several　mainstream　methods.　In　addition,　we　also　carry　out　the　autonomous　grasping　of　seafood　in　a　real　marine　environment　to　verify　the　autonomous　grasping　performance　of　underwater　vehicles.　©　2023,　International　Society　of　Artificial　Life　and　Robotics　(ISAROB).