During　the　detection　of　maritime　targets,　the　jitter　of　the　shipborne　camera　usually　causes　the　video　instability　and　the　false　or　missed　detection　of　targets.　Aimed　at　tackling　this　problem,　a　novel　algorithm　for　maritime　target　detection　based　on　the　electronic　image　stabilization　technology　is　proposed　in　this　study.　The　algorithm　mainly　includes　three　models,　namely　the　points　line　model　(PLM),　the　points　classification　model　(PCM),　and　the　image　classification　model　(ICM).　The　feature　points　(FPs)　are　firstly　classified　by　the　PLM,　and　stable　videos　as　well　as　target　contours　are　obtained　by　the　PCM.　Then　the　smallest　bounding　rectangles　of　the　target　contours　generated　as　the　candidate　bounding　boxes　(bboxes)　are　sent　to　the　ICM　for　classification.　In　the　experiments,　the　ICM,　which　is　constructed　based　on　the　convolutional　neural　network　(CNN),　is　trained　and　its　effectiveness　is　verified.　Our　experimental　results　demonstrate　that　the　proposed　algorithm　outperformed　the　benchmark　models　in　all　the　common　metrics　including　the　mean　square　error　(MSE),　peak　signal　to　noise　ratio　(PSNR),　structural　similarity　index　(SSIM),　and　mean　average　precision　(mAP)　by　at　least　-47.87%,　8.66%,　6.94%,　and　5.75%,　respectively.　The　proposed　algorithm　is　superior　to　the　state-of-the-art　techniques　in　both　the　image　stabilization　and　target　ship　detection,　which　provides　reliable　technical　support　for　the　visual　development　of　unmanned　ships.