  Tracking the abundance of underwater species is crucial for understanding the
effects of climate change on marine ecosystems. Biologists typically monitor
underwater sites with echosounders and visualize data as 2D images (echograms);
they interpret these data manually or semi-automatically, which is
time-consuming and prone to inconsistencies. This paper proposes a deep
learning framework for the automatic detection of schools of herring from
echograms. Experiments demonstrated that our approach outperforms a traditional
machine learning algorithm using hand-crafted features. Our framework could
easily be expanded to detect more species of interest to sustainable fisheries.
