Deep learning methods applied to electronic monitoring data: automated catch event detection for longline fishing Article Swipe
Maoying Qiao
,
Dadong Wang
,
Geoffrey N. Tuck
,
L. Richard Little
,
André E. Punt
,
M Gerner
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/icesjms/fsaa158
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/icesjms/fsaa158
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/icesjms/fsaa158
- https://academic.oup.com/icesjms/article-pdf/78/1/25/36683049/fsaa158.pdf
- OA Status
- bronze
- Cited By
- 19
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3117834500
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3117834500Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/icesjms/fsaa158Digital Object Identifier
- Title
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Deep learning methods applied to electronic monitoring data: automated catch event detection for longline fishingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-08-24Full publication date if available
- Authors
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Maoying Qiao, Dadong Wang, Geoffrey N. Tuck, L. Richard Little, André E. Punt, M GernerList of authors in order
- Landing page
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https://doi.org/10.1093/icesjms/fsaa158Publisher landing page
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https://academic.oup.com/icesjms/article-pdf/78/1/25/36683049/fsaa158.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://academic.oup.com/icesjms/article-pdf/78/1/25/36683049/fsaa158.pdfDirect OA link when available
- Concepts
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Fishing, TRIPS architecture, Computer science, Event (particle physics), Fishery, Range (aeronautics), Data science, Real-time computing, Engineering, Biology, Quantum mechanics, Parallel computing, Physics, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 8, 2023: 4, 2022: 1, 2021: 3Per-year citation counts (last 5 years)
- References (count)
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14Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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