Synthetic　Aperture　Radar　(SAR)　ship　detection　plays　an　increasingly　important　role　in　marine　applications.　With　the　development　of　deep　learning　methods,　many　object　detection　algorithms　based　on　deep　neural　networks　have　emerged.　However,　these　methods　only　obtain　detection　scores　from　a　high-level　convolutional　feature　map,　ignoring　the　multiscale　characteristic　of　objects.　To　solve　the　above　problem,　this　paper　proposes　a　densely　connected　neural　network　based　on　SSD.　Theoretically,　the　high-level　convolutional　feature　maps　contain　more　semantic　information,　while　the　low-level　convolutional　feature　maps　collect　the　detailed　features　of　the　image.　Thus,　we　combine　the　high-　and　low-　level　feature　maps　by　adding　dense　connections　to　SSD,　achieving　the　multiscale　and　multiscene　SAR　ship　detection.　In　addition,　considering　the　characteristics　of　SAR　images,　we　discard　the　image　augmentation　strategy　and　use　K-means　clustering　to　design　the　multiscale　candidate　bounding　boxes,　improving　the　object　detection　accuracy.　Finally,　experiments　carried　out　on　SAR　Ship　Detection　Dataset　(SSDD)　verify　that　the　proposed　method　achieves　satisfactory　performance　on　multiscale　and　multiscene　SAR　ship　detection.　©　2021,　Springer　Nature　Switzerland　AG.