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Lookup NU author(s): Ioannis Polymenis,
Dr Maryam HaroutunianORCiD,
Dr Rosemary NormanORCiD,
Dr David Trodden
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by ASME, 2022.
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The rapid developments in underwater operations by the offshore industry and the scientific community have led to moresophisticated Underwater Vehicles. Notably, many underwater operations such as inspection of underwater structures are done with the help of Autonomous Underwater Vehicles (AUVs). In recent years autonomous vehicles have benefited immensely from the advancements in the area of Artificial Intelligence (AI) and particularly in Deep Learning (DL). Although DL models and applications are extensively used in different areas, such as in Aerial Unmanned Vehicles, Autonomous Car Navigation, and other applications, they are not so widespread in underwater applications mainly because of the difficulties in obtaining underwater datasets for a given application. In this regard, the present work takes the recent advances that have been made in the DL field and creates a custom dataset by using images of objects taken in a controlled, lab environment. Generative Adversarial Networks (GANs) were used to generate underwater images by combining the images taken in a lab and images with underwater environment characteristics. The results showed that it is possible to create such a dataset since the generated images were close to the actual underwater conditions. Consequently, artificial datasets of the underwater environment can overcome the difficulties arising from the limited access to real underwater images and be employed in projects with specific needs in autonomous underwater operations.
Author(s): Polymenis I, Haroutunian M, Norman R, Trodden D
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 41st International Conference on Ocean, Offshore and Arctic Engineering; OMAE2022
Year of Conference: 2022
Online publication date: 13/10/2022
Acceptance date: 03/03/2022
Date deposited: 17/03/2022
ePrints DOI: 10.57711/nbmk-np20
Notes: Paper number: OMAE2022-79891
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