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22 June 2020 Toward robust multitype and orientation detection of vessels in maritime surveillance
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Abstract

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.

© 2020 SPIE and IS&T 1017-9909/2020/$28.00© 2020 SPIE and IS&T
Amir Ghahremani, Egor Bondarev, and Peter H. N. de With "Toward robust multitype and orientation detection of vessels in maritime surveillance," Journal of Electronic Imaging 29(3), 033015 (22 June 2020). https://doi.org/10.1117/1.JEI.29.3.033015
Received: 25 November 2019; Accepted: 5 June 2020; Published: 22 June 2020
JOURNAL ARTICLE
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