Automating jellyfish species recognition through faster region-based convolution neural networks

Abstract

In recent years, citizen science campaigns have provided a very good platform for widespread data collection. Within the marine domain, jellyfish are among the most commonly deployed species for citizen reporting purposes. The timely validation of submitted jellyfish reports remains challenging, given the sheer volume of reports being submitted and the relative paucity of trained staff familiar with the taxonomic identification of jellyfish. In this work, hundreds of photos that were submitted to the “Spot the Jellyfish” initiative are used to train a group of region-based, convolution neural networks. The main aim is to develop models that can classify, and distinguish between, the five most commonly recorded species of jellyfish within Maltese waters. In particular, images of the Pelagia noctiluca, Cotylorhiza tuberculata, Carybdea marsupialis, Velella velella and salps were considered. The reliability of the digital architecture is quantified through the precision, recall, f1 score, and κ score metrics. Improvements gained through the applicability of data augmentation and transfer learning techniques, are also discussed. Very promising results, that support upcoming aspirations to embed automated classification methods within online services, including smart phone apps, were obtained. These can reduce, and potentially eliminate, the need for human expert intervention in validating citizen science reports for the five jellyfish species in question, thus providing prompt feedback to the citizen scientist submitting the report.