Kumar, Shivnesh and Lal, Rajinesh and Ali, Arshaque and Rollings, Nicholas and Mehta, Utkal V. and Assaf, Mansour (2024) A Real - Time Fish Recognition Using Deep Learning Algorithms for Low - Quality Images on an Underwater Drone. In: Artificial Intelligence and Sustainable Computing. Algorithms for Intelligent Systems . Springer Nature, Singapore, pp. 67-79. ISBN 978-981-97-0326-5
Full text not available from this repository.Abstract
The maritime industry employs various tools, including underwater drones, to search for and monitor marine life. The primary objective of this study was to contribute to the preservation of the natural environment by investigating the species of fish present in different bodies of water. The research used three deep learning algorithms: Haar Cascade, Single-Shot Multibox Detector, and inception method for automatic fish detection. The study targeted Clark’s Anemone fish, Moorish Idols, and Blue Devils for species detection. Through simulations, the three approaches demonstrated high precision and efficient recognition times, achieving a maximum accuracy of 73% for fish recognition and 58% for species detection.
| Item Type: | Book Chapter |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Mansour Assaf |
| Date Deposited: | 20 Jan 2025 00:56 |
| Last Modified: | 20 Jan 2025 00:56 |
| URI: | https://repository.usp.ac.fj/id/eprint/14600 |
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