AstroVision: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learning
AstroVision: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learning
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DOI：
10.1016/j.actaastro.2023.01.009
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Travis Driver;K. Skinner;Mehregan Dor;P. Tsiotras
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文献类型：
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作者：
Travis Driver;K. Skinner;Mehregan Dor;P. Tsiotras
小天体飞行任务在很大程度上依赖于光学特征跟踪，以确定目标天体的特征并对其进行相对导航。虽然深度学习在特征检测和描述方面取得了巨大进步，但由于大规模注释数据集的可用性有限，因此训练和验证空间应用的数据驱动模型具有挑战性。本文介绍了AstroVision，这是一个大规模的数据集，由115，970个密集注释的真实的图像组成，这些图像是在过去和正在进行的任务中捕获的16个不同的小天体。我们利用AstroVision开发了一套标准化的基准测试，并对手工制作和数据驱动的特征检测和描述方法进行了详尽的评估。接下来，我们使用AstroVision对最先进的深度特征检测和描述网络进行端到端训练，并在多个基准测试中展示了改进的性能。完整的基准测试管道和数据集将公开提供，以促进空间应用计算机视觉算法的进步。
Missions to small celestial bodies rely heavily on optical feature tracking for characterization of and relative navigation around the target body. While deep learning has led to great advancements in feature detection and description, training and validating data-driven models for space applications is challenging due to the limited availability of large-scale, annotated datasets. This paper introduces AstroVision, a large-scale dataset comprised of 115,970 densely annotated, real images of 16 different small bodies captured during past and ongoing missions. We leverage AstroVision to develop a set of standardized benchmarks and conduct an exhaustive evaluation of both handcrafted and data-driven feature detection and description methods. Next, we employ AstroVision for end-to-end training of a state-of-the-art, deep feature detection and description network and demonstrate improved performance on multiple benchmarks. The full benchmarking pipeline and the dataset will be made publicly available to facilitate the advancement of computer vision algorithms for space applications.