  In recent years, deep learning has been successfully applied in various
scientific domains. Following these promising results and performances, it has
recently also started being evaluated in the domain of radio astronomy. In
particular, since radio astronomy is entering the Big Data era, with the advent
of the largest telescope in the world - the Square Kilometre Array (SKA), the
task of automatic object detection and instance segmentation is crucial for
source finding and analysis. In this work, we explore the performance of the
most affirmed deep learning approaches, applied to astronomical images obtained
by radio interferometric instrumentation, to solve the task of automatic source
detection. This is carried out by applying models designed to accomplish two
different kinds of tasks: object detection and semantic segmentation. The goal
is to provide an overview of existing techniques, in terms of prediction
performance and computational efficiency, to scientists in the astrophysics
community who would like to employ machine learning in their research.
