{"refrec":{"BRefID":390970,"RR":"<b>Martin, A.; Rosset, N.; Blettery, J.; Gousseau, Y.</b> (2023). Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept. <i>Cybium 47(3)</i>: 335-341. <a href=\"https://dx.doi.org/10.26028/cybium/2023-021\" target=\"_blank\">https://dx.doi.org/10.26028/cybium/2023-021</a>","BEntID":388717,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>Cybium 47(3)</i>: 335-341. <a href=\"https://dx.doi.org/10.26028/cybium/2023-021\" target=\"_blank\">https://dx.doi.org/10.26028/cybium/2023-021</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Martin, A.; Rosset, N.; Blettery, J.; Gousseau, Y.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Martin, A. <i>et al.</i>","Englishabstract":"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. 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