Browse by author
Lookup NU author(s): Ioannis Polymenis, Dr Maryam HaroutunianORCiD, Dr Rosemary NormanORCiD, Dr David Trodden
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Underwater Vehicles have become more sophisticated driven by the off-shore sector and the scientific community’s rapid advancements in underwater operations. Notably, many underwater tasks, including the assessment of subsea infrastructure, are performed with the assistance of Autonomous Underwater Vehicles (AUVs). Despite recent breakthroughs in Artificial Intelligence (AI) and notably Deep Learning (DL) models and applications, which have widespread usage in a variety of fields, including aerial unmanned vehicles, autonomous car navigation, and other applications, they are not as prevalent in underwater applications due to the difficulty of getting underwater datasets for a specific application. In this sense, the current study utilises recent advancements in the area of DL to construct a bespoke dataset generated from photographs of items captured in a laboratory environment. Generative Adversarial Networks (GANs) were utilised to translate the laboratory object dataset into the underwater domain by combining the collected images with photographs containing the underwater environment. The findings demonstrated the feasibility of creating such a dataset since the resulting images closely resembled the real underwater environment when compared with real-world underwater ship hull images. Therefore, the artificial datasets of the underwater environment can overcome the difficulties arising from the limited access to real-world underwater images and are used to enhance underwater operations through underwater object image classification and detection.
Author(s): Polymenis I, Haroutunian M, Norman RA, Trodden DG
Publication type: Article
Publication status: Published
Journal: Journal of Marine Science and Engineering
Year: 2022
Volume: 10
Issue: 9
Online publication date: 13/09/2022
Acceptance date: 07/09/2022
Date deposited: 31/10/2022
ISSN (electronic): 2077-1312
Publisher: MDPI AG
URL: https://doi.org/10.3390/jmse10091289
DOI: 10.3390/jmse10091289
Altmetrics provided by Altmetric
