Advancing Ship Re-Identification in the Wild: The ShipReID-2400 Benchmark Dataset and D2InterNet Baseline Method

Ship Re-Identification (ReID) aims to accurately identify ships with the same identity across different times and camera views, playing a crucial role in intelligent waterway transportation. However, compared to the widely researched pedestrian and vehicle ReID, Ship ReID has received much less attention, primarily due to the scarcity of large-scale and high-quality ship ReID datasets available for public access. Moreover, several unique challenges make ship ReID particularly difficult: ships are large objects that are hard to capture fully, and the visible area of ships vary significantly due to changes in cargo loading or water surface conditions. These challenges make it difficult to achieve ideal results by directly applying existing ReID methods. To address these challenges, in this paper, we introduce ShipReID-2400, a dataset for ship ReID compiled from a real-world intelligent waterway traffic monitoring system. It comprises 17,241 images of 2,400 distinct ship identities collected over 53 months, ensuring diversity and representativeness. Furthermore, we propose the Disentangle-to-Interact Network ( D2InterNet ), a simple but strong baseline for ship ReID designed to extract discriminative local features despite significant scale variations. Extensive experimental results show that D2InterNet achieves state-of-the-art performance on both the ShipReID-2400 and VesselReID datasets. In addition, despite being designed for ship ReID, D2InterNet also achieves competitive results on the MSMT17 pedestrian ReID dataset, showcasing its good generalization capability. Our dataset and code are publicly available at https://github.com/HuiGuanLab/ShipReID-2400.