Deep learning methods applied to electronic monitoring data : Automated catch event detection for longline fishing

Journal article


Qiao, Maoying, Wang, Dadong, Tuck, Geoffrey N., Little, L. Richard, Punt, Andre E. and Gerner, Mike. (2021). Deep learning methods applied to electronic monitoring data : Automated catch event detection for longline fishing. ICES Journal of Marine Science: journal du conseil. 78(1), pp. 25-35. https://doi.org/10.1093/icesjms/fsaa158
AuthorsQiao, Maoying, Wang, Dadong, Tuck, Geoffrey N., Little, L. Richard, Punt, Andre E. and Gerner, Mike
Abstract

Electronic monitoring (EM) systems have become functional and cost-effective tools for the conservation and sustainable harvesting of marine resources. EM is an alternative to on-board observers, which produces video segments that can subsequently be reviewed by analysts. It is currently used in a range of fisheries. There are two major challenges to the widespread adoption of EM. One is the large storage requirement for the video footage recorded and the other is the long time required by analysts to review the video footage. We propose an automated catch event detection framework to address these challenges. Our solution, based on deep learning techniques, automatically extracts video segments of catch events, which substantially reduces storage space and review time by analysts. Here, we demonstrate the framework using video footage from three longline fishing trips. The system recalled nearly 100% of the catch events across all trips.

Keywordsartificial intelligence; artificial neural networks; deep learning; fisheries management; machine learning
Year2021
JournalICES Journal of Marine Science: journal du conseil
Journal citation78 (1), pp. 25-35
PublisherOxford University Press
ISSN1054-3139
Digital Object Identifier (DOI)https://doi.org/10.1093/icesjms/fsaa158
Scopus EID2-s2.0-85112018572
Research or scholarlyResearch
Page range25-35
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online27 Dec 2020
Publication process dates
Accepted01 Aug 2020
Deposited02 Aug 2022
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