多卫星视角融合提升低轨卫星目标检测精度，助力空间安全
Collaborative Space Object Detection with Multi-Satellite Viewpoints in LEO Constellations
- 设计多视角输入管道，将多星图像融合输入YOLO检测器
- 三视图融合使mAP50提升至0.732，mAP50-95达0.276
- 适用于低轨星座的实时空间态势感知系统
随着低地球轨道（LEO）星座卫星数量增长，近地空间日益拥挤，空间物体检测（SOD）成为保障空间安全与可持续性的紧迫挑战。为降低碰撞风险并确保空间任务连续性，SOD系统需在严苛星上约束下实现快速准确的检测。本文研究基于深度学习框架的多视角观测融合在提升SOD性能方面的潜力。设计了实用的多视角处理流程及多种输入表示方式，用于馈入基于YOLO的检测器。实验表明，多数情况下多视角输入可行且表现更优：以YOLOv9-m为例，单视角相比三视图融合RGB设置，mAP50从0.638升至0.732，mAP50-95从0.227增至0.276；最佳三视图灰度配置相较单视角，mAP50提升36.3%，mAP50-95提升46.5%。结果证实多视角融合是提升SOD的有效策略，对LEO星座部署下的空间态势感知具有广泛意义。
原文摘要 · Abstract (English)
With the growing number of satellites in low Earth orbit (LEO) constellations, the near-Earth space environment has become increasingly congested, making space object detection (SOD) a pressing challenge for space safety and sustainability. To mitigate collision risks and ensure the continuity of space operations, SOD systems must deliver fast and accurate detection under stringent onboard constraints. In this paper, we investigate the potential of multi-viewpoint observation fusion within a deep learning (DL) framework to enhance SOD performance. We design a practical multi-view pipeline and several input representations for feeding multi-view data into YOLO-based detectors. Our experiments show that using multi-view inputs is feasible in most cases and typically produces better results for mAP50 and mAP50-95. For example, in model YOLOv9-m, single-view compared to a three-view fused RGB setting, mAP50 increases from 0.638 to 0.732, while mAP50-95 improves from 0.227 to 0.276. Compared with the single-view setting, the best three-view grayscale configuration improves mAP50 by 36.3% and mAP50-95 by 46.5%. These findings establish multi-view fusion as a viable and effective strategy for SOD, with broad implications for space situational awareness in LEO constellation deployments.
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