AI 中文总结
针对卫星目标检测的小目标与宽幅场景挑战，研究构建SkySeaLand基准数据集，评估12种主流检测器并提出超轻量SkyDet基线模型，为低内存交通检测提供参考。
AI 中文摘要
卫星目标检测面临小目标和宽幅场景的挑战，标准方形输入的缩放会导致细节丢失。我们推出SkySeaLand，这是一个包含1307张高分辨率卫星图像和19101个已验证边界框的公开数据集，涵盖陆地和海洋场景中的飞机、船只、汽车和船舶类别，提供原生COCO和YOLO格式的标注。该数据集以大尺寸源图像和宽场景几何结构为主：84.5%的图像最长边超过3836像素，73.1%的图像宽高比接近3:1。我们使用通用划分和COCO指标评估了来自YOLO、RT-DETR、DETR和Faster R-CNN系列的12种检测器，测试的YOLO和RT-DETR变体获得了84.4至88.2的mAP50，在报告的特定模型配置下，参数数量更大并未带来一致的精度提升。我们还提出了SkyDet，这是一个参数规模为1.22 M的无锚基线模型，在4.90 MB的内存占用下获得了60.5的mAP50和24.32的mAP50-95，在Tesla T4上的延迟为13.74毫秒（即72.8 FPS）。SkySeaLand为陆地-海洋混合交通检测提供了紧凑的基准，而SkyDet则建立了一个有记录的低内存占用参考，而非追求最先进的精度。
英文摘要
Satellite object detection is challenged by small targets and wide-format scenes that lose detail under standard square-input resizing. We introduce SkySeaLand, a public dataset of 1,307 high-resolution satellite images and 19,101 verified bounding boxes across airplane, boat, car, and ship classes in terrestrial and maritime scenes. Native COCO and YOLO annotations are provided. The collection is dominated by large source images and wide scene geometry: 84.5 percent exceed 3,836 pixels on the longest side and 73.1 percent are near a 3:1 aspect ratio. We evaluate twelve detectors from the YOLO, RT-DETR, DETR, and Faster R-CNN families using a common split and COCO metrics. The tested YOLO and RT-DETR variants obtain 84.4--88.2 mAP50, with no consistent accuracy gain from larger parameter counts under the reported model-specific recipes. We also report SkyDet, a 1.22 M parameter anchor-free baseline that obtains 60.5 mAP50 and 24.32 mAP50-95 in a 4.90 MB footprint, with 13.74 ms latency (72.8 FPS) on a Tesla T4. SkySeaLand provides a compact benchmark for mixed land--maritime transportation detection, while SkyDet establishes a documented low-footprint reference rather than a state-of-the-art accuracy claim.
