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Volume: 31 | Article ID: art00017
Multi-Class detection and orientation recognition of vessels in maritime surveillance
  DOI :  10.2352/ISSN.2470-1173.2019.11.IPAS-266  Published OnlineJanuary 2019

For maritime surveillance, collecting information about vessels and their behavior is of vital importance. This implies reliable vessel detection and determination of the viewing angle to a vessel, which can help in analyzing the vessel behavior and in re-identification. This paper presents a vessel classification and orientation recognition system for maritime surveillance. For this purpose, we have established two novel multi-class vessel detection and vessel orientation datasets, provided to open public access. Each dataset contains 10,000 training and 1,000 evaluation images with 31,078 vessel labels (10 vessel types and 5 orientation classes). We deploy VGG/SSD to train two separate CNN models for multi-class detection and for orientation recognition of vessels. Both trained models provide a reliable F1 score of 82% and 76%, respectively.

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Amir Ghahremani, Yitian Kong, Egor Bondarev, Peter H.N. de With, "Multi-Class detection and orientation recognition of vessels in maritime surveillancein Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVII,  2019,  pp 266-1 - 266-5,

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