The capability to detect boulders on the surface of small bodies is
beneficial for vision-based applications such as hazard detection during
critical operations and navigation. This task is challenging due to the wide
assortment of irregular shapes, the characteristics of the boulders population,
and the rapid variability in the illumination conditions. Moreover, the lack of
publicly available labeled datasets for these applications damps the research
about data-driven algorithms. In this work, the authors provide a statistical
characterization and setup used for the generation of two datasets about
boulders on small bodies that are made publicly available.

研究意在检测小天体表面上的巨石，为视觉导航等应用提供数据驱动算法，通过提供两个公开标注数据集，解决了砝码缺乏等问题。

DOORS: 岩石分割数据集。统计属性与 Blender 设置

DOORS: Dataset fOr bOuldeRs Segmentation. Statistical properties and Blender setup

Low light conditions in aerial images adversely affect the performance of
several vision based applications. There is a need for methods that can
efficiently remove the low light attributes and assist in the performance of
key vision tasks. In this work, we propose a new method that is capable of
enhancing the low light image in a self-supervised fashion, and sequentially
apply detection and segmentation tasks in an end-to-end manner. The proposed
method occupies a very small overhead in terms of memory and computational
power over the original algorithm and delivers superior results. Additionally,
we propose the generation of a new low light aerial dataset using GANs, which
can be used to evaluate vision based networks for similar adverse conditions.

在此工作中，我们提出了一种新的方法，能够自我监督地增强低光图像，并以端对端的方式依次应用检测和分割任务，该方法在内存和计算能力方面占用非常小的开销，并提供了优异的结果，此外，我们还提出使用 GAN 生成新的低光航空数据集，可用于评估类似的不良条件下的视觉网络。