Collaborative Space Object Detection with Multi-Satellite Viewpoints in LEO Constellations

• URL: http://arxiv.org/abs/2606.01895v1
• Date: Mon, 01 Jun 2026 08:37:43 GMT
• Title: Collaborative Space Object Detection with Multi-Satellite Viewpoints in LEO Constellations
• Abstract summary: We investigate the potential of multi-viewpoint observation fusion within a deep learning (DL) framework to enhance space object detection (SOD)<n>Our experiments show that using multi-view inputs is feasible in most cases and typically produces better results for mAP50 and mAP50-95.<n>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.
• Score: 27.362381081726422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Are VLMs Lost Between Sky and Space? LinkS$^2$Bench for UAV-Satellite Dynamic Cross-View Spatial Intelligence [107.79496500634691]
  LinkS$2$Bench links 1,022 minutes of dynamic UAV footage with high-resolution satellite imagery covering over 200 km$2$.<n>Through an LMM-assisted pipeline and rigorous human annotation, we constructed 17.9k high-quality question-answer pairs.<n> Evaluations reveal a substantial gap compared to human baselines, identifying accurate cross-view dynamic alignment as the critical bottleneck.
  arXiv  Detail & Related papers  (2026-04-02T13:22:57Z)
• SDD-YOLO: A Small-Target Detection Framework for Ground-to-Air Anti-UAV Surveillance with Edge-Efficient Deployment [10.723885979419954]
  We propose SDD-YOLO, a small-target detection framework tailored for G2A anti-UAV surveillance.<n>To capture fine-grained spatial details critical for micro-targets, SDD-YOLO introduces a P2 high-resolution detection head operating at 4 times downsampling.<n>Experiments demonstrate that SDD-YOLO-n achieves a mAP@0.5 of 86.4% on DroneSOD-30K, surpassing the YOLOv5n baseline by 7.8 percentage points.
  arXiv  Detail & Related papers  (2026-03-26T09:17:39Z)
• SCAFusion: A Multimodal 3D Detection Framework for Small Object Detection in Lunar Surface Exploration [18.857802421595235]
  This paper presents SCAFusion, a multimodal 3D object detection model tailored for lunar robotic missions.<n>With negligible increase in parameters, our model achieves 69.7% mAP and 72.1% NDS on the nuScenes validation set.<n>In simulated lunar environments built on Isaac Sim, SCAFusion achieves 90.93% mAP, outperforming the baseline by 11.5%.
  arXiv  Detail & Related papers  (2025-12-27T07:08:03Z)
• RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics [53.053660003572965]
  We propose RoboTracer, a 3D-aware VLM that first achieves both 3D spatial referring and measuring.<n>RoboTracer advances multi-step metric-grounded reasoning via reinforcement fine-tuning.<n>We present TraceSpatial-Bench, a challenging benchmark to evaluate spatial tracing.
  arXiv  Detail & Related papers  (2025-12-15T18:52:43Z)
• UAV-MM3D: A Large-Scale Synthetic Benchmark for 3D Perception of Unmanned Aerial Vehicles with Multi-Modal Data [47.317955428393134]
  We introduce UAV-MM3D, a high-fidelity multimodal synthetic dataset for low-altitude UAV perception and motion understanding.<n>It comprises 400K synchronized frames across diverse scenes (urban areas, suburbs, forests, coastal regions) and weather conditions.<n>Each frame provides 2D/3D bounding boxes, 6-DoF poses, and instance-level annotations, enabling core tasks related to UAVs such as 3D detection, pose estimation, target tracking, and short-term trajectory forecasting.
  arXiv  Detail & Related papers  (2025-11-27T12:30:28Z)
• Toward Onboard AI-Enabled Solutions to Space Object Detection for Space Sustainability [29.817805350971366]
  This paper investigates the feasibility and effectiveness of employing vision sensors for space object detection.<n>It introduces models based on the Squeeze-and-Excitation (SE) layer, Vision Transformer (ViT) and the Generalized Efficient Layer Aggregation Network (GELAN)<n> Experimental results show that the proposed models achieve mean average precision at intersection over union threshold 0.5 (mAP50) scores of up to 0.751 and mean average precision averaged over intersection over union thresholds from 0.5 to 0.95 (mAP50:95) scores of up to 0.280.
  arXiv  Detail & Related papers  (2025-05-03T01:56:52Z)
• Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark [15.405137983083875]
  Aerial-ground cooperation offers a promising solution by integrating UAVs' aerial views with ground vehicles' local observations.<n>This paper presents a comprehensive solution for aerial-ground cooperative 3D perception through three key contributions.
  arXiv  Detail & Related papers  (2025-03-10T07:00:07Z)
• Sensing for Space Safety and Sustainability: A Deep Learning Approach with Vision Transformers [29.817805350971366]
  This paper discusses the satellite object detection (SOD) tasks and onboard deep learning (DL) approach to the tasks.<n>Two new DL models are proposed, called GELAN-ViT and GELAN-RepViT, which incorporate vision transformer (ViT) into the Generalized Efficient Layer Aggregation Network (GELAN) architecture.<n>These models outperform the state-of-the-art YOLOv9-t in terms of mean average precision (mAP) and computational costs.
  arXiv  Detail & Related papers  (2024-12-12T03:51:50Z)
• SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection [68.18620488664187]
  We propose a simple yet effective Semi-supervised Oriented Object Detection method termed SOOD++.<n> Specifically, we observe that objects from aerial images usually have arbitrary orientations, small scales, and dense distribution.<n>Extensive experiments conducted on various oriented object under various labeled settings demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-07-01T07:03:51Z)
• A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery [94.78943497436492]
  We present YOLO-S, a simple, fast and efficient network for small target detection. YOLO-S exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation. YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
  arXiv  Detail & Related papers  (2022-04-05T16:29:49Z)
• Disentangle Your Dense Object Detector [82.22771433419727]
  Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding. However, the current training pipeline for dense detectors is compromised to lots of conjunctions that may not hold. We propose Disentangled Dense Object Detector (DDOD), in which simple and effective disentanglement mechanisms are designed and integrated into the current state-of-the-art detectors.
  arXiv  Detail & Related papers  (2021-07-07T00:52:16Z)
• Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks [51.05735925326235]
  A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
  arXiv  Detail & Related papers  (2020-05-26T05:39:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.