CMShipReID: A Cross-Modality Ship Dataset for the Reidentification Task

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.