  Solar Radio Bursts (SRBs) are generally observed in dynamic spectra and have
five major spectral classes, labelled Type I to Type V depending on their shape
and extent in frequency and time. Due to their complex characterisation, a
challenge in solar radio physics is the automatic detection and classification
of such radio bursts. Classification of SRBs has become fundamental in recent
years due to large data rates generated by advanced radio telescopes such as
the LOw-Frequency ARray, (LOFAR). Current state-of-the-art algorithms implement
the Hough or Radon transform as a means of detecting predefined parametric
shapes in images. These algorithms achieve up to 84% accuracy, depending on the
Type of radio burst being classified. Other techniques include procedures that
rely on Constant-FalseAlarm-Rate detection, which is essentially detection of
radio bursts using a de-noising and adaptive threshold in dynamic spectra. It
works well for a variety of different Types of radio bursts and achieves an
accuracy of up to 70%. In this research, we are introducing a methodology named
You Only Look Once v2 (YOLOv2) for solar radio burst classification. By using
Type III simulation methods we can train the algorithm to classify real Type
III solar radio bursts in real-time at an accu
