Recognizing　objects　from　camera　images　is　an　important　field　for　researching　smart　ships　and　intelligent　navigation.　In　sea　transportation,　navigation　marks　indicating　the　features　of　navigational　environments　(e.g.　channels,　special　areas,　wrecks,　etc.)　are　focused　in　this　paper.　A　fine-grained　classification　model　named　RMA　(ResNet-Multiscale-Attention)　based　on　deep　learning　is　proposed　to　analyse　the　subtle　and　local　differences　among　navigation　mark　types　for　the　recognition　of　navigation　marks.　In　the　RMA　model,　an　attention　mechanism　based　on　the　fusion　of　feature　maps　with　three　scales　is　proposed　to　locate　attention　regions　and　capture　discriminative　characters　that　are　important　to　distinguish　the　slight　differences　among　similar　navigation　marks.　Experimental　results　on　a　dataset　with　10260　navigation　mark　images　showed　that　the　RMA　has　an　accuracy　about　96　&　x0025;　to　classify　42　types　of　navigation　marks,　and　the　RMA　is　better　than　ResNet-50　model　with　which　the　accuracy　is　about　94　&　x0025;.　The　visualization　analyses　showed　that　the　RMA　model　can　extract　the　attention　regions　and　the　characters　of　navigation　marks.