|dc.description.abstract||The task of visual classification, done until not long ago by specialists through direct observation, has recently benefited from advancements in the field of computer vision, specifically due to statistical optimization algorithms, such as deep neural networks. In spite of their many advantages, these algorithms require a considerable amount of training data to produce meaningful results. Another downside is that neural networks are usually computationally demanding algorithms, with millions (if not tens of millions) of parameters, which restricts their deployment on low-power embedded field equipment.
In this paper, we address the classification of multiple species of pelagic fish by using small convolutional networks to process images as well as videos frames. We show that such networks, even with little more than 12,000 parameters and trained on small datasets, provide relatively high accuracy (almost 42% for six fish species) in the classification task. Moreover, if the fish images come from videos, we deploy a simple object tracking algorithm to augment the data, increasing the accuracy to almost 49\% for six fish species. The small size of our convolutional networks enables their deployment on relatively limited devices.||