Loading...
Deep learning methods applied to electronic monitoring data : Automated catch event detection for longline fishing
Qiao, Maoying Wang, Dadong Tuck, Geoffrey N. Little, L. Richard Punt, Andre E. Gerner, Mike
Qiao, Maoying
Wang, Dadong
Tuck, Geoffrey N.
Little, L. Richard
Punt, Andre E.
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.
Keywords
artificial intelligence, artificial neural networks, deep learning, fisheries management, machine learning
Date
2021
Type
Journal article
Journal
Book
Volume
78
Issue
1
Page Range
25-35
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled