Multi-class detection and orientation recognition of vessels in maritime surveillance

Onderzoeksoutput: Bijdrage aan congresPaperAcademic

1 Citaat (Scopus)
1 Downloads (Pure)

Samenvatting

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.

Originele taal-2Engels
Pagina's266-1-266-5
Aantal pagina's5
DOI's
StatusGepubliceerd - 13 jan 2019
EvenementIS&T International Symposium on Electronic Imaging 2019, Image Processing: Algorithms and Systems XVII - Burlingame, Verenigde Staten van Amerika
Duur: 13 jan 201917 jan 2019
Congresnummer: XVII
http://www.imaging.org/site/IST/IST/Conferences/EI/EI_2019/Conference/C_IPAS.aspx

Congres

CongresIS&T International Symposium on Electronic Imaging 2019, Image Processing: Algorithms and Systems XVII
Verkorte titelIPAS2019
LandVerenigde Staten van Amerika
StadBurlingame
Periode13/01/1917/01/19
Internet adres

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  • Citeer dit

    Ghahremani, A., Kong, Y., Bondarau, Y., & de With, P. H. N. (2019). Multi-class detection and orientation recognition of vessels in maritime surveillance. 266-1-266-5. Paper gepresenteerd op IS&T International Symposium on Electronic Imaging 2019, Image Processing: Algorithms and Systems XVII, Burlingame, Verenigde Staten van Amerika. https://doi.org/10.2352/ISSN.2470-1173.2019.11.IPAS-266