Optimization of Neural Networks Classification for Disaster-Damage Assessment Using Unmanned Aerial Vehicles System
Location
Session 1 (Room 1302)
Session Format
Oral Presentation
Your Campus
Statesboro Campus- Henderson Library, April 20th
Academic Unit
Department of Electrical and Computer Engineering
Research Area Topic:
Engineering and Material Sciences - Electrical
Co-Presenters and Faculty Mentors or Advisors
Dr. Rami J. Haddad
Abstract
Natural disasters are recurrent weather phenomena whose occurrence has increased worldwide in the past few decades. These disasters cause devastating effects on transportation routes by causing significant damage and obstruction on frequently traveled roads. This research focuses on optimizing the classification accuracy of an autonomous network of unmanned aerial vehicles (UAVs) for transportation disaster management using convolutional neural networks (CNNs). The autonomous network of UAVs will allow first responders to optimize their rescue plans by providing relevant information on inaccessible roads. The research seeks to explore different methods to optimize the architecture of convolutional neural networks for the multiclass classification of disaster-damaged roads.
Program Description
This research focuses on increasing the classification accuracy of convolutional neural networks in an autonomous network of unmanned aerial vehicles for transportation disaster management. The autonomous network of UAVs will allow first responders to optimize their rescue plans by providing relevant information on inaccessible roads. The research seeks to explore different methods to optimize the architecture of convolutional neural networks for the multiclass classification of disaster-damaged roads.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Presentation Type and Release Option
Presentation (Open Access)
Start Date
4-20-2022 11:00 AM
End Date
4-20-2022 12:00 PM
Recommended Citation
Gonzalez Bocanegra, Maria Isabel, "Optimization of Neural Networks Classification for Disaster-Damage Assessment Using Unmanned Aerial Vehicles System" (2022). GS4 Georgia Southern Student Scholars Symposium. 75.
https://digitalcommons.georgiasouthern.edu/research_symposium/2022/2022/75
Optimization of Neural Networks Classification for Disaster-Damage Assessment Using Unmanned Aerial Vehicles System
Session 1 (Room 1302)
Natural disasters are recurrent weather phenomena whose occurrence has increased worldwide in the past few decades. These disasters cause devastating effects on transportation routes by causing significant damage and obstruction on frequently traveled roads. This research focuses on optimizing the classification accuracy of an autonomous network of unmanned aerial vehicles (UAVs) for transportation disaster management using convolutional neural networks (CNNs). The autonomous network of UAVs will allow first responders to optimize their rescue plans by providing relevant information on inaccessible roads. The research seeks to explore different methods to optimize the architecture of convolutional neural networks for the multiclass classification of disaster-damaged roads.
