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French, Geoff, Mackiewicz, Michal 
ORCID: https://orcid.org/0000-0002-8777-8880, Fisher, Mark, Challis, Mike, Knight, Peter, Robinson, Brian and Bloomfield, Angus
(2018)
JellyMonitor: automated detection of jellyfish in sonar images using neural networks.
In:
Proceedings of the 14th IEEE International Conference on Signal Processing.
The Institute of Electrical and Electronics Engineers (IEEE).
ISBN 978-1-5386-4673-1
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Abstract
JellyMonitor is an self-contained automated system that detects jellyfish blooms and reports their presence. It uses an embedded platform to analyse sonar imagery captured by a sonar imaging device. The software utilises a combination of classic computer vision techniques and deep neural networks to detect and classify objects captured by the sonar imaging device. We report on the development of this system and present results obtained from deploying a prototype.
| Item Type: | Book Section |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Colour and Imaging Lab |
| Depositing User: | LivePure Connector |
| Date Deposited: | 12 Dec 2018 11:30 |
| Last Modified: | 20 Jun 2024 23:54 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/69287 |
| DOI: | 10.1109/ICSP.2018.8652268 |
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