  We present a deep learning, computer vision algorithm constructed for the
purposes of identifying and classifying charged particles in camera image
sensors. We apply our algorithm to data collected by the Distributed Electronic
Cosmic-ray Observatory (DECO), a global network of smartphones that monitors
camera image sensors for the signatures of cosmic rays and other energetic
particles, such as those produced by radioactive decays. The algorithm, whose
core component is a convolutional neural network, achieves classification
performance comparable to human quality across four distinct DECO event
topologies. We apply our model to the entire DECO data set and determine a
selection that achieves $\ge90\%$ purity for all event types. In particular, we
estimate a purity of $95\%$ when applied to cosmic-ray muons. The automated
classification is run on the public DECO data set in real time in order to
provide classified particle interaction images to users of the app and other
interested members of the public.
