Resolution exploration using Two-Dimensional Deep Learning Architectures for Infrared Data Captures

Authors

Presenter(s)

Jonathan Paul Schierl

Files

Description

This project investigates the effectiveness of deep learning architecture as a means of object detection. To determine the accuracy of the developed algorithm, two-dimensional short-wave infrared aerial captures will be used as training data. By analyzing the accuracy of detection rates with varying resolutions, a baseline image quality for accurate detection will begin to emerge.

Publication Date

4-24-2019

Project Designation

Graduate Research

Primary Advisor

Theus H. Aspiras

Primary Advisor's Department

Electrical and Computer Engineering

Keywords

Stander Symposium project

Recommended Citation

"Resolution exploration using Two-Dimensional Deep Learning Architectures for Infrared Data Captures" (2019). Stander Symposium Projects. 1754.
https://ecommons.udayton.edu/stander_posters/1754