  Space observatories such as $\textit{Kepler}$ have provided data that can
potentially revolutionise our understanding of stars. Through detailed
asteroseismic analyses we are capable of determining fundamental stellar
parameters and reveal the stellar internal structure with unprecedented
accuracy. However, such detailed analyses, known as peak bagging, have so far
been obtained for only a small percentage of the observed stars while most of
the scientific potential of the available data remains unexplored. One of the
major challenges in peak bagging is identifying how many solar-like oscillation
modes are visible in a power density spectrum. Identification of oscillation
modes is usually done by visual inspection which is time-consuming and has a
degree of subjectivity. Here, we present a peak detection algorithm specially
suited for the detection of solar-like oscillations. It reliably characterises
the solar-like oscillations in a power density spectrum and estimates their
parameters without human intervention. Furthermore, we provide a metric to
characterise the false positive and false negative rates to provide further
information about the reliability of a detected oscillation mode or the
significance of a lack of detected oscillation modes. The algorithm presented
here opens the possibility for detailed and automated peak bagging of the
thousands of solar-like oscillators observed by $\textit{Kepler}$.
