Optimization of Neural Networks Classification for Disaster-Damage Assessment Using Unmanned Aerial Vehicles System
Location
Statesboro Campus, Russell Union, Room 2052, Session 1
Document Type and Release Option
Thesis Presentation (Open Access)
Faculty Mentor
Dr. Rami J. Haddad
Faculty Mentor Email
rhaddad@georgiasouthern.edu
Presentation Year
2022
Start Date
22-4-2022 11:00 AM
End Date
22-4-2022 12:00 PM
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.
Academic Unit
College of Science and Mathematics
Optimization of Neural Networks Classification for Disaster-Damage Assessment Using Unmanned Aerial Vehicles System
Statesboro Campus, Russell Union, Room 2052, Session 1
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.
