The　rapid　development　of　marine　ranching　in　recent　years　provides　a　new　way　of　tackling　the　global　food　crisis.　However,　the　uncontrolled　expansion　of　coastal　aquaculture　has　raised　a　series　of　environmental　problems.　The　fast　and　accurate　detection　of　raft　will　facilitate　scientific　planning　and　the　precise　management　of　coastal　aquaculture.　A　new　deep　learning-based　approach　called　RaftNet　is　proposed　in　this　study　to　extract　the　coastal　raft　aquaculture　in　Sansha　Bay　using　Landsat　8　OLI　images　accurately.　To　overcome　the　issues　of　turbid　water　environments　and　varying　raft　scales　in　aquaculture　areas,　we　constructed　the　RaftNet　by　modifying　the　UNet　network　with　dual-channel　and　residual　hybrid　dilated　convolution　blocks　to　improve　the　extraction　accuracy.　Meanwhile,　we　adopted　the　well-known　semantic　segmentation　networks　(FCN,　SegNet,　UNet,　UNet++,　and　ResUNet)　as　the　contrastive　approaches　for　the　extraction.　The　results　suggested　that　the　proposed　RaftNet　model　achieves　the　best　accuracy　with　a　precision　of　84.5%,　recall　of　88.1%,　F1-score　of　86.30%,　overall　accuracy　(OA)　of　95.7%,　and　intersection　over　union　(IoU)　of　75.9%.　We　then　utilized　our　RaftNet　to　accurately　extract　a　raft　aquaculture　area　in　Sansha　Bay　from　2014　to　2018　and　quantitatively　analyzed　the　change　in　the　raft　area　over　this　period.　The　results　demonstrated　that　our　RaftNet　is　robust　and　suitable　for　the　precise　extraction　of　raft　aquaculture　with　varying　scales　in　turbid　coastal　waters,　and　the　Kappa　coefficient　and　OA　can　reach　as　high　as　88%　and　97%,　respectively.　Moreover,　the　proposed　RaftNet　will　unleash　a　remarkable　potential　for　long　time-series　and　large-scale　raft　aquaculture　mapping.