  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). There have been 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. However, they are
not as prevalent in underwater applications due to the difficulty of obtaining
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.
