Journal
JOURNAL OF ELECTRONIC IMAGING
Volume 29, Issue 3, Pages -Publisher
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JEI.29.3.033015
Keywords
convolutional neural networks; multiclass detection; vessel detection; maritime surveillance
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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 F-1 scores of 85%, 82%, and 76%, respectively. In addition, the modular combined approach improves the F-1 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. (C) 2020 SPIE and IS&T
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