Convolutional Neural Networks for Risso’s Dolphins Identification

• Main Authors: Rosalia Maglietta, Vito Reno, Rocco Caccioppoli, Emanuele Seller, Stefano Bellomo, Francesca Cornelia Santacesaria, Roberto Colella, Giulia Cipriano, Ettore Stella, Karin Hartman, Carmelo Fanizza, Giovanni Dimauro, Roberto Carlucci
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Cetaceans; classification; deep learning; photo-identification; Risso’s dolphin
• Online Access: https://ieeexplore.ieee.org/document/9078758/

Photo-identification is one of the best practices to estimate the abundance of cetaceans and, as such, it can help to obtain the biological information necessary to decision-making and actions to preserve the marine environment and its biodiversity. The Risso's dolphin is one of the least-known cetacean species on a global scale, and the distinctive scars on its dorsal fin proved to be extremely useful to photo-identify single individuals. The main novelty of this paper is the development of a new method based on deep learning, called Neural Network Pool (NNPool), and specifically devoted to the photo-identification of Risso's dolphins. This new method also includes the unique function of recognizing unknown vs known dolphins in large datasets with no interaction by the user. Moreover, the new version of DolFin catalogue, collecting Risso's dolphins data and photos acquired between 2013-2018 in the Northern Ionian Sea (Central-eastern Mediterranean Sea), is presented and used here to carry out the experiments. Results have been validated using a further data set, containing new images of Risso's dolphins from the Northern Ionian Sea and the Azores, acquired in 2019. The performance of the NNPool appears satisfying and increases proportionally to the number of images available, thus highlighting the importance of building large-scale data set for the application at hand.