USING DEEP LEARNING AND UAV IMAGERY TO DETECT ELKHORN CORAL IN ST. CROIX’S EAST END MARINE PARK

<p>Elkhorn coral, or <em>Acropora palmata</em>, is an important reef building species that promotes species abundance and other ecological services to the communities in the US Virgin Islands. We captured high resolution imagery of a reef in St. Croix’s East End Marine Park using a Wingtra One UAV. We then used deep learning techniques to detect individual coral colonies. We compared two deep learning models, FasterRCNN and MaskRCNN, and found that the models achieved accuracy shores up to 0.78. These scores improved when examining only larger corals in shallow waters. The model was able to both detect Elkhorn coral and distinguish it from other corals and features. This will be a useful method for measuring coral abundance and monitoring the success of restoration efforts.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Masters Thesis

Year dc:date.available

2022

Author dc:creator

• Wyatt, Samuel

Contributors dc:contributor

• George Raber
• Gregory Carter
• Steve Schill

• Coral
• Deep Learning
• Remote Sensing
• Object Detection
• Marine
• Geographic Information Sciences
• Marine Biology
• Physical and Environmental Geography

Repository record dc:identifier

https://aquila.usm.edu/masters_theses/886

OAI identifier oai:identifier

oai:aquila.usm.edu:masters_theses-1949