An Annotated and Classified Maritime Dataset aimed at Machine Learning

In the context of the MSc dissertation "The Multi-Object Tracking with Multimodal Information for Autonomous Surface Vehicles", an annotated and classified maritime dataset was created in order to train a machine learning object detector. This dataset combines images from open-source datasets (such as the Singapore Maritime Dataset and the Kaggle Boat Types Recognition Dataset) while adding data collected with the Sense ASV in two portuguese ports (Leixões and Viana do Castelo). The content is reorganised in 9 categories (ore carrier, bulk carrier, container ship, cruise ship, ferry boat, sail boat, fishing boat, small boat, uncategorized), grouping up some less represented categories from the previous datasets with similar characteristics. The resulting dataset, contains 9044 images and their respective 21170 annotations.