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Computer Vision and Deep Learning Applied to the Photo-identification of Cetaceans

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Measurement for the Sea

Part of the book series: Springer Series in Measurement Science and Technology ((SSMST))

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Abstract

Photo-identification is the non-invasive process of uniquely identifying an individual among a set of individuals, based on the analysis of one or more photos. This is a specific task in cetaceans’ abundance and distribution studies, which can be effectively automated using computer vision and deep learning algorithms in large-scale studies. In this chapter, recent advances in the photo-identification of Risso’s dolphins are presented, covering the process from manual approaches to modern deep learning techniques. This manuscript highlights the strong multidisciplinary approach that is mandatory to accelerate and bring innovations working in multiple domains (marine biology and computer science in this case study). Particular attention is also given to the importance of data sharing, especially because it can be seen as a mandatory step that enables the proficient use of modern deep learning approaches to photo-identify a specimen. In the first part of the chapter, we present the state-of-the-art methods currently applied to the photo-identification task; the second part is devoted to describing the Smart Photo-Identification of Risso’s dolphins (SPIR) methods developed by our research team. Finally, future perspectives and directions of this research are discussed.

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Author information

Authors and Affiliations

  1. Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy (CNR STIIMA), Bari, Italy

    Vito Renò & Rosalia Maglietta

  2. Department of Computer Science, University of Bari, Bari, Italy

    Giovanni Dimauro

  3. Jonian Dolphin Conservation, Taranto, Italy

    Carmelo Fanizza

  4. Department of Biology, University of Bari, Bari, Italy

    Roberto Carlucci

Authors
  1. Vito Renò
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  2. Giovanni Dimauro
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  3. Carmelo Fanizza
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  4. Roberto Carlucci
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  5. Rosalia Maglietta
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Corresponding author

Correspondence to Roberto Carlucci .

Editor information

Editors and Affiliations

  1. Department of Engineering, University of Sannio, Benevento, Italy

    Pasquale Daponte

  2. Department of Mechanical, Energy, Management and Transportation Engineering, Università degli Studi di Genova, Genoa, Italy

    Giovanni Battista Rossi

  3. Department of Science and Technology, Parthenope University of Naples, Naples, Italy

    Vincenzo Piscopo

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Renò, V., Dimauro, G., Fanizza, C., Carlucci, R., Maglietta, R. (2022). Computer Vision and Deep Learning Applied to the Photo-identification of Cetaceans. In: Daponte, P., Rossi, G.B., Piscopo, V. (eds) Measurement for the Sea. Springer Series in Measurement Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-82024-4_12

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