  Time-resolved photometry of tens of thousands of red giant stars from space
missions like Kepler and K2 has created the need for automated asteroseismic
analysis methods. The first and most fundamental step in such analysis, is to
identify which stars show oscillations. It is critical that this step can be
performed with no, or little, detection bias, particularly when performing
subsequent ensemble analyses that aim to compare properties of observed stellar
populations with those from galactic models. Yet, an efficient, automated
solution to this initial detection step has still not been found, meaning that
expert visual inspection of data from each star is required to obtain the
highest level of detections. Hence, to mimic how an expert eye analyses the
data, we use supervised deep learning to not only detect oscillations in red
giants, but also predict the location of the frequency at maximum power,
$\nu_{\mathrm{max}}$, by observing features in 2D images of power spectra. By
training on Kepler data, we benchmark our deep learning classifier against K2
data that are given detections by the expert eye, achieving a detection
accuracy of 98% on K2 Campaign 6 stars and a detection accuracy of 99% on K2
Campaign 3 stars. We further find that the estimated uncertainty of our deep
learning-based $\nu_{\mathrm{max}}$ predictions is about 5%. This is comparable
to human-level performance using visual inspection. When examining outliers we
find that the deep learning results are more likely to provide robust
$\nu_{\mathrm{max}}$ estimates than the classical model-fitting method.
