Collaborative Space Object Detection with Multi-Satellite Viewpoints in LEO Constellations
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors studied how combining images from multiple viewpoints can improve detecting satellites and space objects in low Earth orbit. They tested different ways to feed multiple camera views into deep learning detectors based on YOLO models. Their results showed that using multiple views generally gives more accurate detections compared to using just one view. This approach could help make space operations safer by better identifying objects in crowded orbital environments.
Low Earth Orbit (LEO)Space Object Detection (SOD)Deep LearningYOLO DetectorMulti-view FusionMean Average Precision (mAP)Space Situational AwarenessSatellite Constellations
Authors
Xingyu Qu, Wenxuan Zhang, Peng Hu
Abstract
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.