Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept ...
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...
Main Authors: | , , , |
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Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
Cybium
2023
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Subjects: | |
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 |
Summary: | 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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