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Improved accuracy for automated counting of a fish in baited underwater videos for stock assessment1.81 MB
Published VersionCC BY-NC V4.0, Open Access
Journal article
Improved Accuracy for Automated Counting of a Fish in Baited Underwater Videos for Stock Assessment
Frontiers in Marine Science, Vol.8, pp.1-7
2021
Abstract
The ongoing need to sustainably manage fishery resources can benefit from fishery-independent monitoring of fish stocks. Camera systems, particularly baited remote underwater video system (BRUVS), are a widely used and repeatable method for monitoring relative abundance, required for building stock assessment models. The potential for BRUVS-based monitoring is restricted, however, by the substantial costs of manual data extraction from videos. Computer vision, in particular deep learning (DL) models, are increasingly being used to automatically detect and count fish at low abundances in videos. One of the advantages of BRUVS is that bait attractants help to reliably detect species in relatively short deployments (e.g., 1 h). The high abundances of fish attracted to BRUVS, however, make computer vision more difficult, because fish often obscure other fish. We build upon existing DL methods for identifying and counting a target fisheries species across a wide range of fish abundances. Using BRUVS imagery targeting a recovering fishery species, Australasian snapper (Chrysophrys auratus), we tested combinations of three further mathematical steps likely to generate accurate, efficient automation: (1) varying confidence thresholds (CTs), (2) on/off use of sequential non-maximum suppression (Seq-NMS), and (3) statistical correction equations. Output from the DL model was more accurate at low abundances of snapper than at higher abundances (>15 fish per frame) where the model over-predicted counts by as much as 50%. The procedure providing the most accurate counts across all fish abundances, with counts either correct or within 1–2 of manual counts (R2 = 88%), used Seq-NMS, a 45% CT, and a cubic polynomial corrective equation. The optimised modelling provides an automated procedure offering an effective and efficient method for accurately identifying and counting snapper in the BRUV footage on which it was tested. Additional evaluation will be required to test and refine the procedure so that automated counts of snapper are accurate in the survey region over time, and to determine the applicability to other regions within the distributional range of this species. For monitoring stocks of fishery species more generally, the specific equations will differ but the procedure demonstrated here could help to increase the usefulness of BRUVS.
Details
- Title
- Improved Accuracy for Automated Counting of a Fish in Baited Underwater Videos for Stock Assessment
- Authors
- Rod M Connolly (Corresponding Author) - Griffith UniversityDavid V Fairclough (Author) - Department of Primary Industries and Regional DevelopmentEric L Jinks (Author) - Griffith UniversityEllen M Ditria (Author) - Griffith UniversityGary Jackson (Author) - Department of Primary Industries and Regional DevelopmentSebastian Lopez-Marcano (Author) - Griffith UniversityAndrew D Olds (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringKristin I Jinks (Author) - Griffith University
- Publication details
- Frontiers in Marine Science, Vol.8, pp.1-7
- Publisher
- Frontiers Research Foundation
- Date published
- 2021
- DOI
- 10.3389/fmars.2021.658135
- ISSN
- 2296-7745
- Copyright note
- Copyright © 2021 Connolly, Fairclough, Jinks, Ditria, Jackson, Lopez-Marcano, Olds and Jinks. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99582708902621
- Output Type
- Journal article
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