  Weak Gaussian perturbations on a plane wave background could trigger lots of
rogue waves, due to modulational instability. Numerical simulations showed that
these rogue waves seemed to have similar unit structure. However, to the best
of our knowledge, there is no relative result to prove that these rogue waves
have the similar patterns for different perturbations, partly due to that it is
hard to measure the rogue wave pattern automatically. In this work, we address
these problems from the perspective of computer vision via using deep neural
networks. We propose a Rogue Wave Detection Network (RWD-Net) model to
automatically and accurately detect RWs on the images, which directly indicates
they have the similar computer vision patterns. For this purpose, we herein
meanwhile have designed the related dataset, termed as Rogue Wave Dataset-$10$K
(RWD-$10$K), which has $10,191$ RW images with bounding box annotations for
each RW unit. In our detection experiments, we get $99.29\%$ average precision
on the test splits of the RWD-$10$K dataset. Finally, we derive our novel
metric, the density of RW units (DRW), to characterize the evolution of
Gaussian perturbations and obtain the statistical results on them.
