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Journal articles
A Deep learning method for accurate and fast identification of coral reef fishes in underwater images
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 seconds. 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.
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01884005
Contributor : Marc Chaumont <>
Submitted on : Saturday, September 29, 2018 - 12:48:36 PM
Last modification on : Monday, June 29, 2020 - 11:02:03 PM
Document(s) archivé(s) le : Monday, December 31, 2018 - 10:02:14 AM
Contributor : Marc Chaumont <>
Submitted on : Saturday, September 29, 2018 - 12:48:36 PM
Last modification on : Monday, June 29, 2020 - 11:02:03 PM
Document(s) archivé(s) le : Monday, December 31, 2018 - 10:02:14 AM
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