CMShipReID: A Cross-Modality Ship Dataset for the Reidentification Task
Year: 2025
Type: article
Abstract: Image-based ship target analysis is an important task for ship monitoring and maritime security. Previous studies achieved remarkable results in ship detection and recognition tasks but mainly relied on single-modality datasets. So far, no ship re-identification dataset has been collected, restricting crossmodality re-identification research. To solve this problem, we construct the first cross-modality ship re-identification dataset, CMShipReID, which contains visible light, near-infrared, and thermal infrared modalities collected by UAV. It consists of 10 categories, about 138 identifications, and 8337 images. This dataset provides rich and high-quality data support for crossmodal ship re-identification research. We use mainstream ReID algorithms as benchmarks for dataset-related research. The dataset will be publicly available when the paper is accepted.
Authors: Congan Xu, Long Gao, Yu Liu, Qi Zhang, Nan Su, Shaoxuan Zhang, Tianyu Li, Xiaomei Zheng
Institution: Harbin Engineering University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,800
Awards: G2335384894, G8279367643, G8727522050
OpenAlex ID: W4409129353