Deep neural network recognition of shallow water corals in the Gulf of Eilat (Aqaba)

We describe the application of the computerized deep learning methodology to the recognition of corals in a shallow reef in the Gulf of Eilat, Red Sea. This project is aimed at applying deep neural network analysis, based on thousands of underwater images, to the automatic recognition of some common...

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Vydané v:Scientific reports Ročník 10; číslo 1; s. 12959
Hlavní autori: Raphael, Alina, Dubinsky, Zvy, Iluz, David, Benichou, Jennifer I. C., Netanyahu, Nathan S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 31.07.2020
Nature Publishing Group
Predmet:
631/158
631/158/2445
Anthropogenic factors
Bioremediation
Classification
Climate change
Climate effects
Coral reefs
Corals
Deep learning
Genetic variability
Global climate
Humanities and Social Sciences
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Shallow water
Species
ISSN:2045-2322, 2045-2322
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Shrnutí:We describe the application of the computerized deep learning methodology to the recognition of corals in a shallow reef in the Gulf of Eilat, Red Sea. This project is aimed at applying deep neural network analysis, based on thousands of underwater images, to the automatic recognition of some common species among the 100 species reported to be found in the Eilat coral reefs. This is a challenging task, since even in the same colony, corals exhibit significant within-species morphological variability, in terms of age, depth, current, light, geographic location, and inter-specific competition. Since deep learning procedures are based on photographic images, the task is further challenged by image quality, distance from the object, angle of view, and light conditions. We produced a large dataset of over 5,000 coral images that were classified into 11 species in the present automated deep learning classification scheme. We demonstrate the efficiency and reliability of the method, as compared to painstaking manual classification. Specifically, we demonstrated that this method is readily adaptable to include additional species, thereby providing an excellent tool for future studies in the region, that would allow for real time monitoring the detrimental effects of global climate change and anthropogenic impacts on the coral reefs of the Gulf of Eilat and elsewhere, and that would help assess the success of various bioremediation efforts.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-69201-w