  This paper shows the application of autonomous Crater Detection using the
U-Net, a Fully-Convolutional Neural Network, on Ceres. The U-Net is trained on
optical images of the Moon Global Morphology Mosaic based on data collected by
the LRO and manual crater catalogues. The Moon-trained network will be tested
on Dawn optical images of Ceres: this task is accomplished by means of a
Transfer Learning (TL) approach. The trained model has been fine-tuned using
100, 500 and 1000 additional images of Ceres. The test performance was measured
on 350 never before seen images, reaching a testing accuracy of 96.24%, 96.95%
and 97.19%, respectively. This means that despite the intrinsic differences
between the Moon and Ceres, TL works with encouraging results. The output of
the U-Net contains predicted craters: it will be post-processed applying global
thresholding for image binarization and a template matching algorithm to
extract craters positions and radii in the pixel space. Post-processed craters
will be counted and compared to the ground truth data in order to compute image
segmentation metrics: precision, recall and F1 score. These indices will be
computed, and their effect will be discussed for tasks such as automated crater
cataloguing and optical navigation.
