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Title: Studying fish near ocean energy devices using underwater video

Abstract

The effects of energy devices on fish populations are not well-understood, and studying the interactions of fish with tidal and instream turbines is challenging. To address this problem, we have evaluated algorithms to automatically detect fish in underwater video and propose a semi-automated method for ocean and river energy device ecological monitoring. The key contributions of this work are the demonstration of a background subtraction algorithm (ViBE) that detected 87% of human-identified fish events and is suitable for use in a real-time system to reduce data volume, and the demonstration of a statistical model to classify detections as fish or not fish that achieved a correct classification rate of 85% overall and 92% for detections larger than 5 pixels. Specific recommendations for underwater video acquisition to better facilitate automated processing are given. The recommendations will help energy developers put effective monitoring systems in place, and could lead to a standard approach that simplifies the monitoring effort and advances the scientific understanding of the ecological impacts of ocean and river energy devices.

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1434881
Report Number(s):
PNNL-SA-127299
WC0101000
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: OCEANS 2017 MTS/IEEE, September 18-28, 2017, Ancorage, Alaska, 1-7
Country of Publication:
United States
Language:
English

Citation Formats

Matzner, Shari, Hull, Ryan E., Harker-Klimes, Genevra EL, and Cullinan, Valerie I. Studying fish near ocean energy devices using underwater video. United States: N. p., 2017. Web.
Matzner, Shari, Hull, Ryan E., Harker-Klimes, Genevra EL, & Cullinan, Valerie I. Studying fish near ocean energy devices using underwater video. United States.
Matzner, Shari, Hull, Ryan E., Harker-Klimes, Genevra EL, and Cullinan, Valerie I. Mon . "Studying fish near ocean energy devices using underwater video". United States. doi:.
@article{osti_1434881,
title = {Studying fish near ocean energy devices using underwater video},
author = {Matzner, Shari and Hull, Ryan E. and Harker-Klimes, Genevra EL and Cullinan, Valerie I.},
abstractNote = {The effects of energy devices on fish populations are not well-understood, and studying the interactions of fish with tidal and instream turbines is challenging. To address this problem, we have evaluated algorithms to automatically detect fish in underwater video and propose a semi-automated method for ocean and river energy device ecological monitoring. The key contributions of this work are the demonstration of a background subtraction algorithm (ViBE) that detected 87% of human-identified fish events and is suitable for use in a real-time system to reduce data volume, and the demonstration of a statistical model to classify detections as fish or not fish that achieved a correct classification rate of 85% overall and 92% for detections larger than 5 pixels. Specific recommendations for underwater video acquisition to better facilitate automated processing are given. The recommendations will help energy developers put effective monitoring systems in place, and could lead to a standard approach that simplifies the monitoring effort and advances the scientific understanding of the ecological impacts of ocean and river energy devices.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Sep 18 00:00:00 EDT 2017},
month = {Mon Sep 18 00:00:00 EDT 2017}
}

Conference:
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