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Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.
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| one publication added to basket [331773] |
Otolith identification using a deep hierarchical classification model
Stock, M.; Nguyen, B.; Courtens, W.; Verstraete, H.; Stienen, E.; De Baets, B. (2021). Otolith identification using a deep hierarchical classification model. Comput. Electron. Agric. 180: 105883. https://dx.doi.org/10.1016/j.compag.2020.105883
In: Computers and Electronics in Agriculture. Elsevier: Amsterdam. ISSN 0168-1699; e-ISSN 1872-7107, meer
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| Author keywords |
Otolith identification, Seabird diet, Deep learning, Hierarchical softmax |
| Abstract |
The diet of seabirds can yield important insights into the status of economically and ecologically important fish. By analyzing the otoliths found in the birds’ droppings, researchers can observe which fish the birds eat in which abundances. However, identifying the species based on an otolith image is quite labor-intensive and requires particular expertise. In this work, we show that a deep convolutional neural network can identify six fish species with high accuracy. We show that this deep learning approach outperforms more traditional methods and is also more accessible to set up in practice. By exploiting the hierarchy in the species labels, we impose a structure on the prediction probabilities, leading to a remarkable improvement compared to a conventional artificial neural network. Importantly, we can attain good results using only a modest dataset, demonstrating that such approaches are feasible for small-scale and specialized projects. |
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