Date of Award

12-2023

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Ocean Engineering and Marine Sciences

First Advisor

Stephen Wood

Second Advisor

Robert J. Weaver

Third Advisor

Ryan T. White

Fourth Advisor

Richard B. Aronson

Abstract

Ocean exploration has surged in popularity and significance in recent years, including diverse areas like maritime archeology, underwater resources, and submerged structure inspection. The activities mentioned above heavily depend on vision and imagery, a challenge in the unpredictable marine world. This thesis presents a conditional generative adversarial network model for image-to-image translation problems. We designed and trained the model with the end goal of enhancing underwater images. Five metrics were employed for validation to quantify our model’s resulting enhanced images. By doing so, we aim to establish a pipeline that can leverage aerial computer vision algorithms for marine applications.

Our approach demonstrates enhanced results when utilizing aerial classification models, such as Yolov8 and VGG19, when compared to supplying raw underwater images directly. Furthermore, it was discovered that the model efficiency offers the ability for real-time image enhancement.

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