Honors Theses
Advisor
Theus Aspiras
Department
Electrical and Computer Engineering
Publication Date
12-16-2020
Document Type
Honors Thesis
Abstract
This research would develop a method of more accurately detecting objects using machine learning. There is plenty of current research and algorithms to tackle this problem. Our approach would use a dataset gathered with 2-Dimensional Infrared Imagery as well as 3-Dimensional LiDAR Data. We would develop a deep learning network with the ability to “learn” using both of these datasets. This proposed fusion network will perform better than either of the individual networks.
Permission Statement
This item is protected by copyright law (Title 17, U.S. Code) and may only be used for noncommercial, educational, and scholarly purposes.
Keywords
Undergraduate research
Disciplines
Electrical and Computer Engineering | Engineering
eCommons Citation
Schierl, Jonathan P., "Multi-modal Data Analysis and Fusion for Classification in 2D/3D Sensing" (2020). Honors Theses. 303.
https://ecommons.udayton.edu/uhp_theses/303
