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 reidentification dataset has been collected, restricting cross-modality reidentification research. To solve this problem, we construct the first cross-modality ship reidentification dataset, CMShipReID, which contains visible light, near-infrared, and thermal infrared modalities collected by autonomous aerial vehicle. It consists of ten categories, about 138 identifications, and 8337 images. This dataset provides rich and high-quality data support for cross-modal ship reidentification research. We use mainstream reidentification algorithms as benchmarks of this research field.