Date of Award


Degree Type


Degree Name

Master of Science in Ocean Engineering


Ocean Engineering

First Advisor

Christopher Roman


Object detection algorithms have emerged as powerful tools in various domains, yet their application to oceanographic images remains in its early stages. Particularly, the use of stereo camera systems for capturing images of macroscopic marine organisms is still considered novel imagery. Therefore, it is critical to understand how well deep learning algorithms perform in classifying marine organisms in these types of images. This thesis aims to contribute to the nascent literature on this task in the context of stereo camera imagery. The study evaluates the classification performance of two prominent deep learning models, YOLOv5 and ResNet50, when applied to images of macroscopic marine organisms captured in a low-light environment. The findings in this thesis aim to provide insights into the effectiveness of deep learning algorithms when applied to images of this nature, and might serve as a resource for researchers seeking to implement a deep learning model for their specific use case.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.