  In this paper, we propose a deep learning-based model to detect extratropical
cyclones (ETCs) of northern hemisphere, while developing a novel workflow of
processing images and generating labels for ETCs. We first label the cyclone
center by adapting an approach from Bonfanti et.al. [1] and set up criteria of
labeling ETCs of three categories: developing, mature, and declining stages. We
then propose a framework of labeling and preprocessing the images in our
dataset. Once the images and labels are ready to serve as inputs, we create our
object detection model named Single Shot Detector (SSD) to fit the format of
our dataset. We train and evaluate our model with our labeled dataset on two
settings (binary and multiclass classifications), while keeping a record of the
results. Finally, we achieved relatively high performance with detecting ETCs
of mature stage (mean Average Precision is 86.64%), and an acceptable result
for detecting ETCs of all three categories (mean Average Precision 79.34%). We
conclude that the single-shot detector model can succeed in detecting ETCs of
different stages, and it has demonstrated great potential in the future
applications of ETC detection in other relevant settings.
