  Crater detection can provide valuable information for humans to explore the
topography and understand the history of extraterrestrial planets. Due to the
significantly varying scenario distributions, existing detection models trained
on known labelled crater datasets are hardly effective when applied to new
unlabelled planets. To address this issue, we propose a two-stage adaptive
network (TAN) for semi-supervised cross-domain crater detection. Our network is
built on the YOLOv5 detector, where a series of strategies are employed to
enhance its cross-domain generalisation ability. In the first stage, we propose
an attention-based scale-adaptive fusion (ASAF) strategy to handle objects with
significant scale variances. Furthermore, we propose a smoothing hard example
mining (SHEM) loss function to address the issue of overfitting on hard
examples. In the second stage, we propose a sort-based pseudo-labelling
fine-tuning (SPF) strategy for semi-supervised learning to mitigate the
distributional differences between source and target domains. For both stages,
we employ weak or strong image augmentation to suit different cross-domain
tasks. Experimental results on benchmark datasets demonstrate that the proposed
network can enhance domain adaptation ability for crater detection under
varying scenario distributions.
