Intelligent re-recognition algorithm for specific ship target in busy waters under the actual scene

Lv, Jinwen; Chen, Xianqiao; Salah, Mohamed Ben

doi:10.3233/jifs-169762

Cited by 1 publication

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hilton et al investigated the use of a capsule network to address viewpoint invariance in the classification of marine vessels [25]. Jinwen et al introduced a graphic model based on energy loss in the metric-learning phase of a ship recognition algorithm [10]. To our knowledge, there is only one small dataset that has been created for ship re-identification [26].…”

Section: B Marine Vessel Detection and Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Marine Vessel Re-Identification: A Large-Scale Dataset and Global-and-Local Fusion-Based Discriminative Feature Learning

et al. 2020

View full text Add to dashboard Cite

A marine vessel re-identification system has to determine whether or not different images represent the same vessel. Accurate vessel re-identification improves onshore closed-circuit television monitoring in a vessel traffic services system as well as onboard surveillance of surrounding vessels. However, because ships are rigid bodies and the marine environment is harsh, the accurate re-identification of vessels at sea can be very difficult. We describe a marine vessel-re-identification framework, Globaland-Local Fusion-based Multi-view Feature Learning (GLF-MVFL), which is based on a combination of global and fine-grained local features. GLF-MVFL combines cross-entropy loss with our newly-developed orientation-guided quintuplet loss. We exploit intrinsic features of marine vessels to optimize multi-view representation learning for re-identification. GLF-MVFL uses ResNet-50 as the backbone network to extract features for simultaneous quintuple input. It detects and discriminates between features and estimates viewpoints to form a comprehensive re-identification framework. We created an annotated large-scale vessel retrieval dataset, VesselID-539, which contains images from viewpoints similar to those of an autonomous surface vessel, to use in evaluating the performance of the model. Extensive experiments and analysis of the results obtained from using VesselID-539 demonstrate that our approach significantly increases the accuracy of vessel re-identification and is more effective and robust for images from different viewpoints than other approaches. INDEX TERMS Autonomous surface vessel (ASV), maritime surveillance, VesselID-539 dataset, multi views, vessel re-identification (V-ReID).

show abstract

Section: B Marine Vessel Detection and Identificationmentioning

confidence: 99%

“…Re-identification of vessels is similar to the reidentification of pedestrians [2]- [6] or vehicles [12], [14]- [17]. It is important for security surveillance, and in the creation of intelligent transportation systems (ITS) at sea [10]. Fig.…”

Section: Introductionmentioning

confidence: 99%