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Rigoudy, N., Dussert, G., Benyoub, A., Besnard, A., Birck, C., Boyer, J., Bollet, Y., Bunz, Y., Caussimont, G., Chetouane, E., Carriburu, J.C., Cornette, P., Delestrade, A., De Backer, N., Dispan, L., Le Barh, M., Duhayer, J., Elder, J.-F., Fanjul, J.-B., Fonderflick, J., Froustey, N., Garel, M., Gaudry, W., Gérard, A., Gimenez, O., Hemery, A., Hemon, A., Jullien, J.-M., Knitter, D., Malafosse, I., Marginean, M., Ménard, L., Ouvrier, A., Pariset, G., Prunet, V., Rabault, J., Randon, M., Raulet, Y., Régnier, A., Ribière, R., Ricci, J.-C., Ruette, S., Schneylin, Y., Sentilles, J., Siefert, N., Smith, B. 
ORCID: 0000-0002-7435-9265, Terpereau, G., Touchet, P., Thuiller, W., Uzal, A. ORCID: 0000-0001-6478-1895, Vautrain, V., Vimal, R., Weber, J., Spataro, B., Miele, V. and Chamaillé-Jammes, S.,
2023.
The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images.
European Journal of Wildlife Research, 69 (6): 113.
ISSN 1612-4642
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Text
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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.
| Item Type: | Journal article | ||||||
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| Publication Title: | European Journal of Wildlife Research | ||||||
| Creators: | Rigoudy, N., Dussert, G., Benyoub, A., Besnard, A., Birck, C., Boyer, J., Bollet, Y., Bunz, Y., Caussimont, G., Chetouane, E., Carriburu, J.C., Cornette, P., Delestrade, A., De Backer, N., Dispan, L., Le Barh, M., Duhayer, J., Elder, J.-F., Fanjul, J.-B., Fonderflick, J., Froustey, N., Garel, M., Gaudry, W., Gérard, A., Gimenez, O., Hemery, A., Hemon, A., Jullien, J.-M., Knitter, D., Malafosse, I., Marginean, M., Ménard, L., Ouvrier, A., Pariset, G., Prunet, V., Rabault, J., Randon, M., Raulet, Y., Régnier, A., Ribière, R., Ricci, J.-C., Ruette, S., Schneylin, Y., Sentilles, J., Siefert, N., Smith, B., Terpereau, G., Touchet, P., Thuiller, W., Uzal, A., Vautrain, V., Vimal, R., Weber, J., Spataro, B., Miele, V. and Chamaillé-Jammes, S. | ||||||
| Publisher: | Springer Science and Business Media LLC | ||||||
| Date: | December 2023 | ||||||
| Volume: | 69 | ||||||
| Number: | 6 | ||||||
| ISSN: | 1612-4642 | ||||||
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| Rights: | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10344-023-01742-7 | ||||||
| Divisions: | Schools > School of Animal, Rural and Environmental Sciences | ||||||
| Record created by: | Laura Ward | ||||||
| Date Added: | 22 Dec 2023 16:34 | ||||||
| Last Modified: | 20 Oct 2024 03:00 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/50599 |
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