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Title: Automatic optical detection and classification of marine animals around MHK converters using machine vision

Abstract

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robust principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.

Authors:
 [1]
  1. Univ. of Washington, Seattle, WA (United States)
Publication Date:
Research Org.:
Univ. of Washington, Seattle, WA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Water Power Technologies Office (EE-4WP)
Contributing Org.:
H.T. Harvey & Associates
OSTI Identifier:
1416953
Report Number(s):
DOE-UW-0006785
DOE Contract Number:  
EE0006785
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
16 TIDAL AND WAVE POWER; 54 ENVIRONMENTAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Machine learning; marine hydrokinetic converters; machine vision; dimensionality reduction; marine wildlife

Citation Formats

Brunton, Steven. Automatic optical detection and classification of marine animals around MHK converters using machine vision. United States: N. p., 2018. Web. doi:10.2172/1416953.
Brunton, Steven. Automatic optical detection and classification of marine animals around MHK converters using machine vision. United States. doi:10.2172/1416953.
Brunton, Steven. Mon . "Automatic optical detection and classification of marine animals around MHK converters using machine vision". United States. doi:10.2172/1416953. https://www.osti.gov/servlets/purl/1416953.
@article{osti_1416953,
title = {Automatic optical detection and classification of marine animals around MHK converters using machine vision},
author = {Brunton, Steven},
abstractNote = {Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robust principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.},
doi = {10.2172/1416953},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}