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.

海上船只重新识别（re-ID）是在不相交的摄影机视图之间进行船舶身份匹配的计算机视觉任务。船只识别码在监视和海上交通流量分析领域有着突出的应用。但是，该领域遭受的障碍是缺乏能够训练深度学习模型的大规模数据集。在这项研究中，我们提出了一个新的数据集，其中包括729支血管的4614张图像以及5槽定位和8类血管类型注释，以促进进一步的研究。这项研究的第二个贡献是对新数据集的基线re-ID分析。定量比较了10种最新深度学习架构的性能，以揭示最佳实践。最后，我们提出了一种新颖的多分支深度学习架构，即海事船舶Re-ID网络（MVR-net），解决船只重新识别的挑战性问题。在新数据集上对我们的方法进行的评估得出74.5％的mAP和77.9％的Rank-1得分，与最佳表现基准相比，性能提高了5.7％mAP和5.0％的Rank-1。MVR-net的性能也比PRN（领先的车辆re-ID网络）好，分别比mAP和Rank-1高出2.9％和4.3％。