Multi-modal Data Analysis and Fusion for Classification in 2D/3D Sensing

Author(s)

Jonathan P. Schierl, University of Dayton

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