Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept ...

• Main Authors: Martin, Alexis, Rosset, Nicolas, Blettery, Jonathan, Gousseau, Yann
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Cybium 2023
• Subjects: Annotated image collection, Automatic identification, Benthos, Bycatch, Deep-learning, Fisheries, Images, Kerguelen, Macro-invertebrates, Southern Ocean; Southern Ocean; Kerguelen
• Online Access: https://dx.doi.org/10.26028/cybium/2023-021; https://sfi-cybium.fr/fr/using-deep-learning-automatic-identification-images-marine-benthic-macro-invertebrate-bycatch-proof

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. ...