Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Bruce A. Cox, PhD
Abstract
Neural networks, despite their prowess in computer vision, often exhibit "flickering". Flickering occurs when networks fail to maintain consistent object representation across frames, leading to inaccurate and inconsistent output. This problem is particularly critical in mission-surety applications where reliable object recognition is crucial. This research presents a novel approach that combines existing object detection and tracking algorithms like YOLO and SORT with a Bayesian backend model. This Bayesian backend incorporates probabilistic reasoning to analyze the network's confidence in its predictions and infer the most likely object identity across multiple frames, effectively reducing flickering and enhancing robustness.
AFIT Designator
AFIT-ENS-MS-24-M-089
Recommended Citation
Markham, Taylor D., "Bayesian Augmentation of Object Detection Algorithms to Enhance Object Classification Stability" (2024). Theses and Dissertations. 7724.
https://scholar.afit.edu/etd/7724
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.