Methods of Neural Network Analysis to Identify Weapons in Augmented Reality
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science and Info Sys
Date of Award
Summer 2021
Abstract
Augmented reality (AR) is a technology being utilized to help give an individual insightwhile doing their job by overlaying information on the real world in real-time. The military is working on using AR to help soldiers on the battlefield to make more informed decisions. The battlefield is a dynamic environment and requires real-time, clear, and effective ways of getting information to the soldier. Convolutional Neural Networks (CNN) are used in this study to classify images of weapons in videos. The YouTube8M video dataset was explored and another hand-picked image dataset was created based on image content and then used in training three ImageNet pre-trained models, Xception, ResNet50, and MobileNet using transfer learning. The effectiveness of this dataset and models were evaluated for speed and accuracy. We found that ResNet50 was more accurate but slightly slower than MobileNet. The Xception pre-trained base was overfitting even though it performed well on the test data. Further research is discussed.
Advisor
Derek Harter
Subject Categories
Computer Sciences | Physical Sciences and Mathematics
Recommended Citation
Hays, Amy Wilson, "Methods of Neural Network Analysis to Identify Weapons in Augmented Reality" (2021). Electronic Theses & Dissertations. 276.
https://digitalcommons.tamuc.edu/etd/276
