AI-EMPOWERED UAVS FOR RAPID DISASTER RESPONSE AND MANAGEMENT
Advisor | Mohamed, Amr |
Author | AHMED, NEMA |
Available date | 2024-09-10T10:07:26Z |
Publication Date | 2024-06 |
Abstract | Efficient disaster response and survivor detection are essential in minimizing casualties and mitigating the impact of disastrous events. This thesis presents an innovative approach to addressing these challenges through a combination of Transfer Learning and Deep Reinforcement Learning (DRL).We introduce a Transfer Learning framework aimed at enhancing the speed and accuracy of survivor detection in aerial imagery by fine-tuning pre-trained convolutional neural networks (CNNs). Leveraging Transfer Learning significantly reduces computational costs while improving detection performance. Building upon this foundation, a novel DRL-based multi-drone targets visitation system designed for efficient disaster response missions in dynamic environments. Optimal drone deployment strategies are learned through extensive experimentation, and the system's adaptability to varying mission scales, target numbers, and area sizes is evaluated. The proposed framework offers a promising solution to enhance disaster response capabilities, outperforming traditional baseline policies in terms of energy consumption and mission efficiency. This thesis highlights the potential of Artificial Intelligence (AI) empowered Unmanned Aerial Vehicles (UAVs) technology in improving disaster management and guides future investigations in this critical field. |
Language | en |
Subject | Disaster Response Survivor Detection Aerial Imagery Transfer Learning Deep Reinforcement Learning (DRL) Convolutional Neural Networks (CNNs) Multi-Drone Targets Visitation System UAVs (Unmanned Aerial Vehicles) AI (Artificial Intelligence) Disaster Management Emergency Response Search and Rescue Remote Sensing Machine Learning Computer Vision |
Type | Master Thesis |
Department | Computer Science and Engineering |
Files in this item
This item appears in the following Collection(s)
-
Computer Science & Engineering [100 items ]