Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Electrical and Computer Engineering
First Advisor
Scott L. Nykl, PhD
Abstract
This work used the newly released YOLO version 8 Object Detection as a feature detector for a monocular pose estimation pipeline and showed up to an 83.6% reduction in translation error when compared to YOLOv5. YOLOv8’s new Pose task’s utility as a feature detector was also explored. It performed consistently worse than the Object Detection task, with typical translation error magnitudes between 3 and 8 times worse than Object Detection. Finally, we investigated the use of constant-sized bounding box labels for Object Detection. Previous approaches have used Bounding Box Corrections (BBC). We found that using constant sized labels increases the number of accurate predictions made by a pose prediction pipeline by up to 11%, while also reducing label generation complexity.
AFIT Designator
AFIT-ENG-MS-24-M-010
Recommended Citation
Friesenhahn, Dawson, "Comparing YOLOv8 to YOLOv5 for Pose Estimation Supporting Automated Aerial Refueling" (2024). Theses and Dissertations. 7697.
https://scholar.afit.edu/etd/7697
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.