Toggle Main Menu Toggle Search

Open Access padlockePrints

Virtual Underwater Datasets for Autonomous Inspections

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.

Publication metadata

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


DOI: 10.3390/jmse10091289


Altmetrics provided by Altmetric


Funder referenceFunder name