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Journal Articles European Journal of Wildlife Research Year : 2023

The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images

Noa Rigoudy
  • Function : Author
Gaspard Dussert
  • Function : Author
Abdelbaki Benyoub
  • Function : Author
Carole Birck
  • Function : Author
Jérome Boyer
  • Function : Author
Yoann Bollet
  • Function : Author
Yoann Bunz
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Gérard Caussimont
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Elias Chetouane
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Jules Chiffard Carriburu
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Pierre Cornette
  • Function : Author
Anne Delestrade
  • Function : Author
Nina de Backer
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Lucie Dispan
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Maden Le Barh
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Jeanne Duhayer
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Jean-François Elder
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Jean-Baptiste Fanjul
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Jocelyn Fonderflick
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Nicolas Froustey
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Mathieu Garel
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  • PersonId : 984991
William Gaudry
Agathe Gérard
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Arzhela Hemery
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Audrey Hemon
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  • PersonId : 1238398
Jean-Michel Jullien
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Daniel Knitter
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Isabelle Malafosse
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Mircea Marginean
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Louise Ménard
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  • PersonId : 1027125
Gwennaelle Pariset
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Vincent Prunet
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  • PersonId : 987182
Julien Rabault
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Malory Randon
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Yann Raulet
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Antoine Régnier
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Romain Ribière
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Jean-Claude Ricci
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Yann Schneylin
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Jérôme Sentilles
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Nathalie Siefert
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Bethany Smith
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Guillaume Terpereau
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Pierrick Touchet
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Wilfried Thuiller
Antonio Uzal
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Valentin Vautrain
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Ruppert Vimal
Julian Weber
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Vincent Miele
Simon Chamaillé-Jammes

Abstract

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa that are common in Europe, with an emphasis on mammals. The classification model achieved 0.97 validation accuracy and often >0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequences of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera trap images. The DeepFaune initiative is an ongoing project, with new partners joining regularly, which allows us to continuously add new species to the classification model.
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Dates and versions

hal-04278674 , version 1 (10-11-2023)

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Noa Rigoudy, Gaspard Dussert, Abdelbaki Benyoub, Aurélien Besnard, Carole Birck, et al.. The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images. European Journal of Wildlife Research, 2023, 69, 24 p. ⟨10.1007/s10344-023-01742-7⟩. ⟨hal-04278674⟩
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