AIRST-CS

The two modalities were hardware-synchronized, followed by calibration, cross-modal alignment, quality control, and manual annotation. Each image pair is annotated with axis-aligned bounding boxes for two categories: car and ship. Many targets occupy less than 0.5% of the image area, making the dataset suitable for small-object and tiny-object detection research.

AIRST-CS covers urban and waterway scenes, including highways, ports, docks, rivers, and inland waterways, with daytime/nighttime imaging, strong illumination variation, cluttered backgrounds, dense targets, occlusion, and thermal artifacts. The dataset can be used for RGB–infrared object detection, multispectral fusion, modality reliability analysis, robust cross-modal learning, and UAV-based visual perception. Users should follow the official train/validation/test split for reproducible evaluation.