  The capability to detect boulders on the surface of small bodies is
beneficial for vision-based applications such as navigation and hazard
detection during critical operations. 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. The authors address
this challenge by designing a multi-step training approach to develop a
data-driven image processing pipeline to robustly detect and segment boulders
scattered over the surface of a small body. Due to the limited availability of
labeled image-mask pairs, the developed methodology is supported by two
artificial environments designed in Blender specifically for this work. These
are used to generate a large amount of synthetic image-label sets, which are
made publicly available to the image processing community. The methodology
presented addresses the challenges of varying illumination conditions,
irregular shapes, fast training time, extensive exploration of the architecture
design space, and domain gap between synthetic and real images from previously
flown missions. The performance of the developed image processing pipeline is
tested both on synthetic and real images, exhibiting good performances, and
high generalization capabilities
