Sharan, Sumit and *, Harsh and Kininmonth, Stuart and Mehta, Utkal V. (2021) Automated CNN based coral reef classification using image augmentation and deep learning. International Journal of Engineering Intelligent Systems, 29 (4). pp. 253-261. ISSN 1472-8915
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Abstract
A critical issue faced by the marine scientist is to classify underwater images describing coral benthic cover. Typically, scientists take underwater imagery using high-resolution cameras and further analysis on these corals and marine species is done on land (preferably a laboratory) and by visual inspection. However, the analysis is time consuming, since the first step, which is the classification of corals, is an intensive activity by taxonomic experts. This traditional manual classification method is difficult to automate or quicken which is problematic given the high volume of images. In this
work, the fundamental analysis is discussed by using available techniques such as deep learning (DL) and Convolutional Neural Network (CNN). It is required to find an easier, efficient and faster way to automate the classification of corals. This task is complicated since most of the common coral species look similar to one another. For reasons of structural diversity, it is easier to differentiate other forms of marine life such as fish and stingrays. This paper is based on the difficult but important Scleractinian (Stony) corals only. A technique recommended is investigated further at the structural level such as branching corals. Verification result proves that the training and testing data are almost similar, thus the proposed technique is capable to learn and predict correctly.
| Item Type: | Journal Article |
|---|---|
| Uncontrolled Keywords: | Coral classification; CNN; Automation; Deep learning; RGB approach. |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > Robotics and Automation |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Utkal Mehta |
| Date Deposited: | 30 Aug 2021 00:54 |
| Last Modified: | 30 Aug 2021 01:01 |
| URI: | https://repository.usp.ac.fj/id/eprint/12929 |
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