Skip to main content
WCS
Menu
Library
Library Catalog
eJournals & eBooks
WCS Research
Archives
Research Use
Finding Aids
Digital Collections
WCS History
WCS Research
Research Publications
Science Data
Services for WCS Researchers
Archives Shop
Bronx Zoo
Department of Tropical Research
Browse By Product
About Us
FAQs
Intern or Volunteer
Staff
Donate
Search WCS.org
Search
search
Popular Search Terms
WCS History
Library and Archives
Library and Archives Menu
Library
Archives
WCS Research
Archives Shop
About Us
Donate
en
fr
Title
A deep learning method for accurate and fast identification of coral reef fishes in underwater images
Author(s)
Villon, S.;Mouillot, D.;Chaumont, M.;Darling, E. S.;Subsol, G.;Claverie, T.;Villeger, S.
Published
2018
Publisher
Ecological Informatics
Published Version DOI
https://doi.org/10.1016/j.ecoinf.2018.09.007
Abstract
Identifying and counting fish individuals on photos and videos is a crucial task to cost-effectively monitor marine biodiversity, yet it remains difficult and time-consuming. In this paper, we present a method to assist the identification of fish species on underwater images, and we compare our model performances to human ability in terms of speed and accuracy. We first tested the performance of a convolutional neural network (CNN) trained with different photographic databases while accounting for different post-processing decision rules to identify 20 fish species. Finally, we compared the performance of species identification of our best CNN model with that of humans on a test database of 1197 fish images representing nine species. The best CNN was the one trained with 900,000 images including (i) whole fish bodies, (ii) partial fish bodies and (iii) the environment (e.g. reef bottom or water). The rate of correct identification was 94.9%, greater than the rate of correct identification by humans (89.3%). The CNN was also able to identify fish individuals partially hidden behind corals or behind other fish and was more effective than humans to identify fish on smallest or blurry images while humans were better to identify fish individuals in unusual positions (e.g. twisted body). On average, each identification by our best CNN using a common hardware took 0.06 s. Deep Learning methods can thus perform efficient fish identification on underwater images and offer promises to build-up new video-based protocols for monitoring fish biodiversity cheaply and effectively.
Keywords
Marine fishes;Convolutional neural network;Underwater pictures;Machine learning;Automated identification;visual census;neural-networks;video stations;system;vulnerability;temperate;density
Access Full Text
A full-text copy of this article may be available. Please email the
WCS Library
to request.
Back
PUB24253