Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Martin, A.; Rosset, N.; Blettery, J.; Gousseau, Y. (2023). Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept. Cybium 47(3): 335-341. https://dx.doi.org/10.26028/cybium/2023-021
In: Cybium. Muséum national d'Histoire naturelle: Paris. ISSN 0399-0974; e-ISSN 2101-0315, more
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| Author keywords |
Annotated image collection - Automatic identification - Benthos - Bycatch - Deep-learning - Fisheries - Images - Kerguelen - Macro-invertebrates - Southern Ocean |
| Authors | | Top |
- Martin, A.
- Rosset, N.
- Blettery, J.
- Gousseau, Y.
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| Abstract |
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. |
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