Toward robust multitype and orientation detection of vessels in maritime surveillance

Reliable multitype and orientation vessel detection is of vital importance for maritime surveillance. We develop three separate convolutional neural network (CNN) models for high-performance single-class vessel detection and then multiclass vessel-type/orientation detection. We also propose a modular combined network, which enhances the multiclass operation. The initial three models provide reliable F1 scores of 85%, 82%, and 76%, respectively. In addition, the modular combined approach improves the F1 scores for the multitype and orientation vessel detection by 2% and 3%, respectively. The training and testing were done on a dataset, including the multitype/orientation annotations, covering 31,078 vessel labels (10 vessel types and 5 orientations), which is offered to public access.