JellyMonitor: automated detection of jellyfish in sonar images using neural networks

Geoff French; Michal Mackiewicz; Mark Fisher; Mike Challis; Peter Knight; Brian Robinson; Angus Bloomfield

doi:10.1109/ICSP.2018.8652268

JellyMonitor: automated detection of jellyfish in sonar images using neural networks

Geoff French, Michal Mackiewicz, Mark Fisher, Mike Challis, Peter Knight, Brian Robinson, Angus Bloomfield

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

23 Citations (Scopus)

51 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings of the 14th IEEE International Conference on Signal Processing
Publisher	The Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	978-1-5386-4673-1
DOIs	https://doi.org/10.1109/ICSP.2018.8652268
Publication status	Published - Aug 2018

Access to Document

10.1109/ICSP.2018.8652268

Accepted manuscriptAccepted author manuscript, 849 KB

Cite this

French, G., Mackiewicz, M., Fisher, M., Challis, M., Knight, P., Robinson, B., & Bloomfield, A. (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). https://doi.org/10.1109/ICSP.2018.8652268

@inproceedings{0c800626e78c4032934d2f6f8b930e82,

title = "JellyMonitor: automated detection of jellyfish in sonar images using neural networks",

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.",

author = "Geoff French and Michal Mackiewicz and Mark Fisher and Mike Challis and Peter Knight and Brian Robinson and Angus Bloomfield",

year = "2018",

month = aug,

doi = "10.1109/ICSP.2018.8652268",

language = "English",

booktitle = "Proceedings of the 14th IEEE International Conference on Signal Processing",

publisher = "The Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

}

JellyMonitor: automated detection of jellyfish in sonar images using neural networks. / French, Geoff; Mackiewicz, Michal ; Fisher, Mark et al.

Proceedings of the 14th IEEE International Conference on Signal Processing. The Institute of Electrical and Electronics Engineers (IEEE), 2018.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - JellyMonitor: automated detection of jellyfish in sonar images using neural networks

AU - French, Geoff

AU - Mackiewicz, Michal

AU - Fisher, Mark

AU - Challis, Mike

AU - Knight, Peter

AU - Robinson, Brian

AU - Bloomfield, Angus

PY - 2018/8

Y1 - 2018/8

N2 - 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.

AB - 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.

U2 - 10.1109/ICSP.2018.8652268

DO - 10.1109/ICSP.2018.8652268

M3 - Conference contribution

BT - Proceedings of the 14th IEEE International Conference on Signal Processing

PB - The Institute of Electrical and Electronics Engineers (IEEE)

ER -