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 interspecific 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.