构建首个宽幅卫星交通检测数据集，支持海陆混合场景目标识别。
SkySeaLand: A Wide-Format Satellite Transportation Benchmark with an Ultra-Lightweight Detection Baseline
- 提出超轻量无锚框检测模型SkyDet，仅122万参数
- 在宽幅图像上实现60.5 mAP50，4.9MB内存占用
- 适用于边缘设备部署，适合低资源遥感目标检测
卫星目标检测面临小目标和宽幅场景细节丢失的挑战。本文引入SkySeaLand，一个包含1307张高分辨率卫星图像和19101个标注边界框的公开数据集，覆盖飞机、船、车、船舶四类目标，涵盖陆地与海洋场景。图像以大尺寸为主：84.5%的图像最长边超过3836像素，73.1%接近3:1的宽高比。采用统一划分和COCO评估指标，测试了十二种来自YOLO、RT-DETR、DETR和Faster R-CNN系列的检测器，其中YOLO与RT-DETR变体在mAP50上达到84.4–88.2。此外提出SkyDet，一个仅122万参数的无锚框基线模型，在4.90MB内存占用下实现60.5 mAP50、24.32 mAP50-95，Tesla T4上延迟13.74毫秒（72.8 FPS）。SkySeaLand为海陆混合交通检测提供紧凑基准，SkyDet则建立可复现的低功耗参考而非最先进精度。
原文摘要 · Abstract (English)
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.
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