A New Large-Scale Dataset for Marine Vessel Re-Identification Based on Swin Transformer Network in Ocean Surveillance Scenario

Abstract

In recent years, there has been an upward trend that marine vessels, an important object category in marine monitoring, have gradually become a research focal point in the field of computer vision, such as detection, tracking, and classification. Among them, marine vessel re-identification (Re-ID) emerges as a significant frontier research topics, which not only faces the dual challenge of huge intra-class and small inter-class differences, but also has complex environmental interference in the port monitoring scenarios. To propel advancements in marine vessel Re-ID technology, SwinTransReID, a framework grounded in the Swin Transformer for marine vessel Re-ID, is introduced. Specifically, the project initially encodes the triplet images separately as a sequence of blocks and construct a baseline model leveraging the Swin Transformer, achieving better performance on the Re-ID benchmark dataset in comparison to convolution neural network (CNN)-based approaches. And it introduces side information embedding (SIE) to further enhance the robust feature-learning capabilities of Swin Transformer, thus, integrating non-visual cues (orientation and type of vessel) and other auxiliary information (hull colour) through the insertion of learnable embedding modules. Additionally, the project presents VesselReID-1656, the first annotated large-scale benchmark dataset for vessel Re-ID in real-world ocean surveillance, comprising 135,866 images of 1656 vessels along with 5 orientations, 12 types, and 17 colours. The proposed method achieves 87.1$\%$ mAP and 96.1$\%$ Rank-1 accuracy on the newly-labelled challenging dataset, which surpasses the state-of-the-art (SOTA) method by 1.9$\%$ mAP regarding to performance. Moreover, extensive empirical results demonstrate the superiority of the proposed SwinTransReID on the person Market-1501 dataset, vehicle VeRi-776 dataset, and Boat Re-ID vessel dataset.