High Performance Space Debris Tracking in Complex Skylight Backgrounds with a Large-Scale Dataset

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 63,247 frames and covers 27,350 synthetic space debris. Extensive experiments validate the effectiveness of our model and the challenging of our dataset. Furthermore, we annotate a real debris observation test set to evaluate the generalization of the model from simulated data to real observation scenarios. SDT-Net achieves a MOTA score of 76% on this set, which demonstrates its transferability to real-world scenarios. Our dataset and code will be released soon.