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Remote Sens. 2017, 9(2), 100; doi:10.3390/rs9020100

A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery

1
Department of Information Engineering and Computer Science University of Trento, 38123 Trento, Italy
2
Département des Télécommunications, Faculté d’Electronique et d’Informatique, USTHB BP 32, El-Alia, Bab-Ezzouar, 16111 Algiers, Algeria
*
Author to whom correspondence should be addressed.
Academic Editors: Francesco Nex, Xiaofeng Li and Prasad S. Thenkabail
Received: 11 November 2016 / Revised: 29 December 2016 / Accepted: 14 January 2017 / Published: 24 January 2017
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
View Full-Text   |   Download PDF [6738 KB, uploaded 24 January 2017]   |  

Abstract

Following an avalanche, one of the factors that affect victims’ chance of survival is the speed with which they are located and dug out. Rescue teams use techniques like trained rescue dogs and electronic transceivers to locate victims. However, the resources and time required to deploy rescue teams are major bottlenecks that decrease a victim’s chance of survival. Advances in the field of Unmanned Aerial Vehicles (UAVs) have enabled the use of flying robots equipped with sensors like optical cameras to assess the damage caused by natural or manmade disasters and locate victims in the debris. In this paper, we propose assisting avalanche search and rescue (SAR) operations with UAVs fitted with vision cameras. The sequence of images of the avalanche debris captured by the UAV is processed with a pre-trained Convolutional Neural Network (CNN) to extract discriminative features. A trained linear Support Vector Machine (SVM) is integrated at the top of the CNN to detect objects of interest. Moreover, we introduce a pre-processing method to increase the detection rate and a post-processing method based on a Hidden Markov Model to improve the prediction performance of the classifier. Experimental results conducted on two different datasets at different levels of resolution show that the detection performance increases with an increase in resolution, while the computation time increases. Additionally, they also suggest that a significant decrease in processing time can be achieved thanks to the pre-processing step. View Full-Text
Keywords: avalanche; convolutional neural network (CNN); deep learning; hidden Markov model (HMM); object detection; search and rescue operation; support vector machine (SVM); unmanned aerial vehicle (UAV) avalanche; convolutional neural network (CNN); deep learning; hidden Markov model (HMM); object detection; search and rescue operation; support vector machine (SVM); unmanned aerial vehicle (UAV)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bejiga, M.B.; Zeggada, A.; Nouffidj, A.; Melgani, F. A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery. Remote Sens. 2017, 9, 100.

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