  Polarization measurements done using Imaging Polarimeters such as the Robotic
Polarimeter are very sensitive to the presence of artefacts in images.
Artefacts can range from internal reflections in a telescope to satellite
trails that could contaminate an area of interest in the image. With the advent
of wide-field polarimetry surveys, it is imperative to develop methods that
automatically flag artefacts in images. In this paper, we implement a
Convolutional Neural Network to identify the most dominant artefacts in the
images. We find that our model can successfully classify sources with 98\% true
positive and 97\% true negative rates. Such models, combined with transfer
learning, will give us a running start in artefact elimination for near-future
surveys like WALOP.
