Paper Info
Title
Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles
Abstract
Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas. In addition, by utilizing an embedded platform and deep learning UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. To this end, this paper focuses on the automated aerial scene classification of disaster events from on-board a UAV. Specifically, a dedicated Aerial Image Database for Emergency Response (AIDER) applications is introduced and a comparative analysis of existing approaches is performed. Through this analysis a lightweight convolutional neural network (CNN) architecture is developed, capable of running efficiently on an embedded platform achieving ~3x higher performance compared to existing models with minimal memory requirements with less than 2% accuracy drop compared to the state-of-the-art. These preliminary results provide a solid basis for further experimentation towards real-time aerial image classification for emergency response applications using UAVs.
Year
DOI
Venue
2019
10.1109/CVPRW.2019.00077
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Keywords
DocType
Volume
lightweight convolutional neural network architecture,embedded platform,real-time aerial image classification,unmanned aerial vehicles,camera sensors,situational awareness,disaster management applications,disaster stricken area,automated aerial scene classification,disaster events,deep learning UAV,Aerial Image Database for Emergency Response applications,AIDER applications,CNN architecture
Conference
abs/1906.08716
ISSN
ISBN
Citations 
2160-7508
978-1-7281-2507-7
1
PageRank 
References 
Authors
0.36
9
2
Name
Order
Citations
PageRank
Christos Kyrkou110214.05
Theo Theocharides272.75