Maritime vessel re-identification: novel VR-VCA dataset and a multi-branch architecture MVR-net

Amir Ghahremani (Corresponding author), Tunc Alkanat, Egor Bondarev, Peter H.N. de With

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)

Abstract

Maritime vessel re-identification (re-ID) is a computer vision task of vessel identity matching across disjoint camera views. Prominent applications of vessel re-ID exist in the fields of surveillance and maritime traffic flow analysis. However, the field suffers from the absence of a large-scale dataset that enables training of deep learning models. In this study, we present a new dataset that includes 4614 images of 729 vessels along with 5-bin orientation and 8-class vessel-type annotations to promote further research. A second contribution of this study is the baseline re-ID analysis of our new dataset. Performances of 10 recent deep learning architectures are quantitatively compared to reveal the best practices. Lastly, we propose a novel multi-branch deep learning architecture, Maritime Vessel Re-ID network (MVR-net), to address the challenging problem of vessel re-ID. Evaluation of our approach on the new dataset yields 74.5% mAP and 77.9% Rank-1 score, providing a performance increase of 5.7% mAP and 5.0% Rank-1 over the best-performing baseline. MVR-net also outperforms the PRN (a pioneering vehicle re-ID network), by 2.9% and 4.3% higher mAP and Rank-1, respectively.

Original languageEnglish
Article number71
Number of pages14
JournalMachine Vision and Applications
Volume32
Issue number3
DOIs
Publication statusPublished - May 2021

Bibliographical note

Funding Information:
The authors appreciate the nVIDIA Corporation gift of two GPUs for this research. The research is funded by the European H2020 Interreg PASSAnT Project and the Provincial Government of Noord-Brabant, The Netherlands.

Publisher Copyright:
© 2021, The Author(s).

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

The authors appreciate the nVIDIA Corporation gift of two GPUs for this research. The research is funded by the European H2020 Interreg PASSAnT Project and the Provincial Government of Noord-Brabant, The Netherlands.

Keywords

  • CNNs
  • Deep learning
  • Image retrieval
  • Maritime surveillance
  • Maritime vessel re-identification

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