  The shipping industry is an important component of the global trade and
economy, however in order to ensure law compliance and safety it needs to be
monitored. In this paper, we present a novel Ship Type classification model
that combines vessel transmitted data from the Automatic Identification System,
with vessel imagery. The main components of our approach are the Faster R-CNN
Deep Neural Network and a Neuro-Fuzzy system with IF-THEN rules. We evaluate
our model using real world data and showcase the advantages of this combination
while also compare it with other methods. Results show that our model can
increase prediction scores by up to 15.4\% when compared with the next best
model we considered, while also maintaining a level of explainability as
opposed to common black box approaches.
