Date of Award
Fall 12-8-2025
Document Type
Thesis
Publication Status
Version of Record
Submission Date
December 2025
Department
Computer and Electrical Engineering and Computer Science
College Granting Degree
College of Engineering and Computer Science
Department Granting Degree
Electrical Engineering and Computer Science
Degree Name
Master of Science (MS)
Thesis/Dissertation Advisor [Chair]
Xingquan Zhu
Abstract
The Florida manatee (Trichechus manatus latirostris), a threatened marine mammal often found in coastal waters, require accurate monitoring to inform conservation strategies. However, traditional counting methods are hindered by observer bias and surface distortions. Recent advances in artificial intelligence (AI) address these challenges using captured aerial footage of manatees, demonstrating the potential of artificial intelligence for automating manatee counting. Still, various challenges arise hindering accurate counts.
Building on these studies, this work advances artificial intelligence with underwater footage, instance segmentation, data augmentation, and pseudo-labeling to automate manatee counting in data-scarce environments. Three experiments were conducted using 10%, 60%, and 90% labeled training data to evaluate model performance under varying levels of data availability. Across all experiments, the proposed methods outperformed the previous state-of-the-art method, with reductions over 65% in both MAE and MASE, showing that pseudo-labeling effectively mitigates data scarcity.
Recommended Citation
Owchariw, Theodor, "DEEP LEARNING FOR UNDERWATER MANATEE COUNTING AND TRACKING" (2025). Electronic Theses and Dissertations. 214.
https://digitalcommons.fau.edu/etd_general/214