Deep　learning　has　been　used　to　improve　intelligent　transportation　systems　(ITS)　by　classifying　ship　targets　in　interior　waterways.　Researchers　have　created　numerous　classification　methods,　but　they　have　low　accuracy　and　misclassify　other　ship　targets.　As　a　result,　more　research　into　ship　classification　is　required　to　avoid　inland　waterway　collisions.　We　present　a　new　convolutional　neural　network　classification　method　for　inland　waterways　that　can　classify　the　five　major　ship　types:　cargo,　military,　carrier,　cruise,　and　tanker.　This　method　can　also　be　used　for　other　ship　classes.　The　proposed　method　consists　of　four　phases　for　the　boosting　of　classification　accuracy　for　Intelligent　Transport　Systems　(ITS)　based　on　convolutional　neural　networks　(CNNs);　efficient　augmentation　method,　the　hyper-parameter　optimization　(HPO)　technique　for　optimum　CNN　model　parameter　selection,　transfer　learning,　and　ensemble　learning　are　suggested.　All　experiments　used　Kaggle＇s　public　Game　of　Deep　Learning　Ship　dataset.　In　addition,　the　proposed　ship　classification　achieved　98.38%　detection　rates　and　97.43%　F1　scores.　Our　suggested　classification　technique　was　also　evaluated　on　the　MARVEL　dataset.　This　dataset　includes　10,000　image　samples　for　each　class　and　26　types　of　ships　for　generalization.　The　suggested　method　also　delivered　an　excellent　performance　compared　to　other　algorithms,　with　performance　metrics　with　an　accuracy　of　97.04%,　a　precision　of　96.1%,　a　recall　of　95.92%,　a　specificity　of　96.55%,　and　a　96.31%　F1　score.