Abstract
The European Space Agency (ESA), driven by its ambitions on planned lunar missions with the Argonaut lander, has a profound interest in reliable crater detection, since craters pose a risk to safe lunar landings. This task is usually addressed with automated crater detection algorithms (CDA) based on deep learning techniques. It is non-trivial due to the vast amount of craters of various sizes and shapes, as well as challenging conditions such as varying illumination and rugged terrain. Therefore, we propose a deep-learning CDA based on the OWLv2 model, which is built on a Vision Transformer, that has proven highly effective in various computer vision tasks. For fine-tuning, we utilize a manually labeled dataset fom the IMPACT project, that provides crater annotations on high-resolution Lunar Reconnaissance Orbiter Camera Calibrated Data Record images. We insert trainable parameters using a parameter-efficient fine-tuning strategy with Low-Rank Adaptation, and optimize a combined loss function consisting of Complete Intersection over Union (CIoU) for localization and a contrastive loss for classification. We achieve satisfactory visual results, along with a maximum recall of 94.0% and a maximum precision of 73.1% on a test dataset from IMPACT. Our method achieves reliable crater detection across challenging lunar imaging conditions, paving the way for robust crater analysis in future lunar exploration.
Abstract (translated)
欧洲航天局（ESA）因其计划中的月球任务，特别是使用Argonaut着陆器的使命，对可靠的陨石坑检测有着浓厚的兴趣。这是因为陨石坑会威胁到安全的月球登陆。通常，这类问题通过基于深度学习技术的自动化陨石坑检测算法（CDA）来解决。然而，由于各种大小和形状的大量陨石坑以及光照变化和崎岖地形等挑战性条件的存在，这一任务具有相当大的复杂性和难度。 因此，我们提出了一种基于OWLv2模型的深度学习CDA方法，该模型建立在视觉变换器（Vision Transformer）之上，并且已经在多种计算机视觉任务中证明了其高度有效性。为了进行微调，我们利用了一个由IMPACT项目提供的手动标注数据集，它提供了高分辨率月球勘测轨道飞行器相机校准记录图像上的陨石坑注释。我们采用低秩适应的参数高效微调策略来插入可训练参数，并优化一个结合了完全交并比（CIoU）用于定位和对比损失用于分类的组合损失函数。 在IMPACT提供的测试数据集上，我们的方法实现了令人满意的视觉效果以及高达94.0%的最大召回率和73.1%的最大精确度。这种方法能够在具有挑战性的月球成像条件下实现可靠的陨石坑检测，为未来月球探测中的稳健陨石坑分析铺平了道路。
URL
https://arxiv.org/abs/2601.07795
