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.