Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks

In this work the initial steps towards a system capable of parametrising fish schools in underwater images are presented. For this purpose a deep convolutional neural network called Optical Fish Detection Network (OFDNet) is introduced. This is based on state-of-the-art deep learning object detection architectures and carries out the task of fish detection, localization and species classification using visual data obtained by underwater cameras. This work is focused towards applications in the poorly conditioned North and Baltic Sea and is initially developed for the purpose of recognizing herring and mackerel. Based on experiments on a dataset obtained at sea, OFDNet is shown to successfully detect 66.7% of the fish included and furthermore classify 89.7% of these correctly.