  Source finding is one of the most challenging tasks in upcoming radio
continuum surveys with SKA precursors, such as the Evolutionary Map of the
Universe (EMU) survey of the Australian SKA Pathfinder (ASKAP) telescope. The
resolution, sensitivity, and sky coverage of such surveys is unprecedented,
requiring new features and improvements to be made in existing source finders.
Among them, reducing the false detection rate, particularly in the Galactic
plane, and the ability to associate multiple disjoint islands into physical
objects. To bridge this gap, we developed a new source finder, based on the
Mask R-CNN object detection framework, capable of both detecting and
classifying compact, extended, spurious, and poorly imaged sources in radio
continuum images. The model was trained using ASKAP EMU data, observed during
the Early Science and pilot survey phase, and previous radio survey data, taken
with the VLA and ATCA telescopes. On the test sample, the final model achieves
an overall detection completeness above 85\%, a reliability of $\sim$65\%, and
a classification precision/recall above 90\%. Results obtained for all source
classes are reported and discussed.
