用深度学习提升复杂天光背景下的太空碎片追踪精度
High Performance Space Debris Tracking in Complex Skylight Backgrounds with a Large-Scale Dataset
- 提出SDT-Net网络，端到端学习碎片特征表示
- 在62,562帧数据上实现73.2%的MOTA指标
- 适配真实观测场景，适合空间监测研究者
随着太空探索快速发展，太空碎片因潜在重大威胁日益受到关注，亟需实时精准的碎片追踪。现有方法多依赖传统信号处理，难以应对复杂背景与密集碎片。本文提出基于深度学习的太空碎片追踪网络SDT-Net，有效表征碎片特征，提升端到端模型学习的效率与稳定性。为训练与评估该模型，我们通过新型观测仿真方案构建大规模数据集SDTD，包含18,040段视频序列，共62,562帧，涵盖25万条合成碎片。大量实验验证了模型有效性及数据集挑战性。此外，在南极站真实数据上测试，取得73.2%的MOTA分数，证明其强泛化能力。数据集与代码将陆续公开。
原文摘要 · Abstract (English)
With the rapid development of space exploration, space debris has attracted more attention due to its potential extreme threat, leading to the need for real-time and accurate debris tracking. However, existing methods are mainly based on traditional signal processing, which cannot effectively process the complex background and dense space debris. In this paper, we propose a deep learning-based Space Debris Tracking Network~(SDT-Net) to achieve highly accurate debris tracking. SDT-Net effectively represents the feature of debris, enhancing the efficiency and stability of end-to-end model learning. To train and evaluate this model effectively, we also produce a large-scale dataset Space Debris Tracking Dataset (SDTD) by a novel observation-based data simulation scheme. SDTD contains 18,040 video sequences with a total of 62,562 frames and covers 250,000 synthetic space debris. Extensive experiments validate the effectiveness of our model and the challenging of our dataset. Furthermore, we test our model on real data from the Antarctic Station, achieving a MOTA score of 73.2%, which demonstrates its strong transferability to real-world scenarios. Our dataset and code will be released soon.
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