用合成数据训练模型，从模糊图像中恢复航天器并估计姿态。
Deep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects
- 用合成数据+去卷积+U-Net恢复模糊航天器图像
- 结合ResNet50回归网络，姿态误差降低71.9%
- 适合轨道目标识别与碰撞规避研究者
随着地球轨道上航天器密度增加，对其识别、姿态和轨迹的准确判断对避免碰撞及清除空间碎片至关重要。然而，由于缺乏可用于训练的航天器图像数据，相关模型训练面临挑战。本文提出一种生成真实感空间目标（RSO）图像的合成数据框架，以国际空间站（ISS）为例，结合图像回归与图像恢复方法，从模糊图像中估计姿态。实验分析了所提图像恢复与回归技术在真实遥感图像上的表现、改进潜力及局限性。研究采用有效点扩散函数进行图像去卷积，再通过U-Net提取细节特征。结果表明，仅使用U-Net进行图像重建时，平均均方误差降低97.28%，平均角度误差降低71.9%。该方法成功实现国际空间站的姿态估计，验证了多工具融合在远距离轨道目标分析中的有效性。
原文摘要 · Abstract (English)
As the density of spacecraft in Earth's orbit increases, their recognition, pose and trajectory identification becomes crucial for averting potential collisions and executing debris removal operations. However, training models able to identify a spacecraft and its pose presents a significant challenge due to a lack of available image data for model training. This paper puts forth an innovative framework for generating realistic synthetic datasets of Resident Space Object (RSO) imagery. Using the International Space Station (ISS) as a test case, it goes on to combine image regression with image restoration methodologies to estimate pose from blurred images. An analysis of the proposed image recovery and regression techniques was undertaken, providing insights into the performance, potential enhancements and limitations when applied to real imagery of RSOs. The image recovery approach investigated involves first applying image deconvolution using an effective point spread function, followed by detail object extraction with a U-Net. Interestingly, using only U-Net for image reconstruction the best pose performance was attained, reducing the average Mean Squared Error in image recovery by 97.28% and the average angular error by 71.9%. The successful application of U-Net image restoration combined with the Resnet50 regression network for pose estimation of the International Space Station demonstrates the value of a diverse set of evaluation tools for effective solutions to real-world problems such as the analysis of distant objects in Earth's orbit.
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