@article{bibcite_5253,
  author = {Alexis Martin and Nicolas Rosset and Jonathan Blettery and Yann Gousseau},
  title = {Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept},
  abstract = {<p><span class="field-content">We applied a deep-learning approach in order to develop a neural network able to detect and identify macro-invertebrate organisms within images of benthos bycatch collected in the Southern Ocean. We used the Faster RCNN architecture and fine-tuning approach. To perform the transfer-learning, we used an annotated dataset of 59,756 images of organisms identified within 1,845 images of lots, covering eleven taxa: Echinodermata, Asteroidea, Arthropoda, Annelida, Chordata, Hemichordata, Cnidaria, Porifera, Bryozoa, Brachiopoda and Mollusca. The resulting network, not yet efficient enough to obtain precise identifications, is able to provide detection and classification of organisms with a good level of accuracy considering the limited quality of the images used for training. We present this study as a proof of concept for teams involved in the management of collections of macro-invertebrate images.</span></p>},
  year = {2023},
  journal = {Cybium},
  volume = {47},
  chapter = {335},
  pages = {7},
  month = {09/2023},
  url = {https://sfi-cybium.fr/fr/using-deep-learning-automatic-identification-images-marine-benthic-macro-invertebrate-bycatch-proof},
  doi = {https://doi.org/10.26028/cybium/2023-021},
}