A Framework for Wildfire Inspection Using Deep Convolutional Neural Networks

Iuliu Novac, Kenneth Richard Geipel, Jacobo Eduardo de Domingo Gil, Lucas Goncalves de Paula, Kristian Hyttel, Dimitrios Chrysostomou

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Abstrakt

This paper presents the details of a holistic framework designed for wildfire inspection and estimation of its geolocation. The system is built around a low-cost, commercial quadcopter, and the main areas of interest we address in this paper are the semi-autonomous navigation of the drone, the training and classification of fire using deep convolutional neural networks, the estimation of the size and location of the wildfire and the real-time feedback and communication with the user. The evaluation of the functionality of the system demonstrates that with the combination of the proposed techniques we can successfully detect and classify fire in video streams at 19.2 FPS while we can calculate the size and location of the fire with an accuracy of 60.76%.
OriginalsprogEngelsk
TitelIEEE/SICE International Symposium on System Integration
Antal sider6
ForlagIEEE
Publikationsdato9 mar. 2020
Sider867-872
Artikelnummer9026244
ISBN (Trykt)78-1-7281-6668-1
ISBN (Elektronisk)978-1-7281-6667-4
DOI
StatusUdgivet - 9 mar. 2020
BegivenhedIEEE/SICE International Symposium on System Integration - Hawaii Convention Center, Honolulu, USA
Varighed: 12 jan. 202015 jan. 2020

Konference

KonferenceIEEE/SICE International Symposium on System Integration
LokationHawaii Convention Center
LandUSA
ByHonolulu
Periode12/01/202015/01/2020

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