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Open AccessFeature PaperArticle

Combined Color Semantics and Deep Learning for the Automatic Detection of Dolphin Dorsal Fins

1
Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing, National Research Council of Italy (CNR STIIMA), Via Amendola 122 D/O, 70126 Bari, Italy
2
Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
3
Department of Computer Science, University of Bari, Via Orabona 4, 70125 Bari, Italy
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Jonian Dolphin Conservation, Viale Virgilio 102, 74121 Taranto, Italy
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Nova Atlantis Foundation, Risso’s Dolphin Research Centre, Rua Dr. A. F. Pimentel 11, 9930-309 Santa Cruz das Ribeiras, Pico, Azores, Portugal
6
Department of Biology, University of Bari, Via Orabona 4, 70125 Bari, Italy
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 758; https://doi.org/10.3390/electronics9050758
Received: 10 April 2020 / Revised: 28 April 2020 / Accepted: 30 April 2020 / Published: 5 May 2020
(This article belongs to the Section Computer Science & Engineering)
Photo-identification is a widely used non-invasive technique in biological studies for understanding if a specimen has been seen multiple times only relying on specific unique visual characteristics. This information is essential to infer knowledge about the spatial distribution, site fidelity, abundance or habitat use of a species. Today there is a large demand for algorithms that can help domain experts in the analysis of large image datasets. For this reason, it is straightforward that the problem of identify and crop the relevant portion of an image is not negligible in any photo-identification pipeline. This paper approaches the problem of automatically cropping cetaceans images with a hybrid technique based on domain analysis and deep learning. Domain knowledge is applied for proposing relevant regions with the aim of highlighting the dorsal fins, then a binary classification of fin vs. no-fin is performed by a convolutional neural network. Results obtained on real images demonstrate the feasibility of the proposed approach in the automated process of large datasets of Risso’s dolphins photos, enabling its use on more complex large scale studies. Moreover, the results of this study suggest to extend this methodology to biological investigations of different species. View Full-Text
Keywords: photo-identification; cetaceans; Risso; computer vision; deep learning; CNN photo-identification; cetaceans; Risso; computer vision; deep learning; CNN
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Renò, V.; Losapio, G.; Forenza, F.; Politi, T.; Stella, E.; Fanizza, C.; Hartman, K.; Carlucci, R.; Dimauro, G.; Maglietta, R. Combined Color Semantics and Deep Learning for the Automatic Detection of Dolphin Dorsal Fins. Electronics 2020, 9, 758.

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