Publication Date

Spring 2023

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Philip Heller

Second Advisor

Genya Ishigaki

Third Advisor

Nada Attar

Keywords

MASK R-CNN, Coral Reefs

Abstract

Coral Reefs and their diverse array of life forms play a vital role in maintaining the health of our planet's environment. However, due to their fragility, it can be challenging to study the reefs without damaging their delicate ecosystem. To address this issue, researchers have employed non-invasive methods such as using Autonomous Reef Monitoring Structures (ARMS) plates to monitor biodiversity. Data was collected as genetic samples from the plates, and high-resolution photographs were taken. To make the best use of this image data, scientists have turned to machine learning and computer vision. Prior to this study, MASKR-CNN was utilized as a tool to determine general models, but annotating these images proved difficult. A general segmenter would perform poorly overall, and would generally succeed only on the life form it was most trained on. To address this challenge, we have pivoted to using MASK R-CNN to identify certain individual life forms of particular interest much more quickly than developing a general model. This approach shows potential for breaking down the problem into smaller components and resolving them individually.

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