Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats-Reference-Cited by-同舟云学术

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Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats

Published:2020-10-12 Issue:11 Volume:192 Page:
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Ditria Ellen M.^ORCID,Sievers Michael,Lopez-Marcano Sebastian,Jinks Eric L.,Connolly Rod M.

Funder

Australian Research Council

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s10661-020-08653-z.pdf

Reference44 articles.

1. Abrantes, K. G., Barnett, A., Baker, R., & Sheaves, M. (2015). Habitat-specific food webs and trophic interactions supporting coastal-dependent fishery species: An Australian case study. Reviews in Fish Biology and Fisheries, 25(2), 337–363.

2. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., van Essen, B. C., Awwal, A. A. S., & Asari, V. K. (2019). A state-of-the-art survey on deep learning theory and architectures. Electronics, 8(3), 292.

3. Beijbom, O., Edmunds, P. J., Kline, D. I., Mitchell, B. G., & Kriegman, D. (2012). Automated annotation of coral reef survey images. IEEE Conference on Computer Vision and Pattern Recognition, 2012, 1170–1177.

4. Buckland, M., & Gey, F. (1994). The relationship between recall and precision. Journal of the American Society for Information Science, 45(1), 12–19.

5. Christin, S., Hervet, E., & Lecomte, N. (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution, 10(10), 1632–1644.

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