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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

39 Downloads (Pure)

Abstract

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%.
Original languageEnglish
Title of host publicationIEEE/SICE International Symposium on System Integration
Number of pages6
PublisherIEEE
Publication date9 Mar 2020
Pages867-872
Article number9026244
ISBN (Print)78-1-7281-6668-1
ISBN (Electronic)978-1-7281-6667-4
DOIs
Publication statusPublished - 9 Mar 2020
EventIEEE/SICE International Symposium on System Integration - Hawaii Convention Center, Honolulu, United States
Duration: 12 Jan 202015 Jan 2020

Conference

ConferenceIEEE/SICE International Symposium on System Integration
LocationHawaii Convention Center
CountryUnited States
CityHonolulu
Period12/01/202015/01/2020

Keywords

  • DCNN
  • quadcopter navigation
  • wildfire inspection
  • drone control
  • deep convolutional neural networks

Fingerprint Dive into the research topics of 'A Framework for Wildfire Inspection Using Deep Convolutional Neural Networks'. Together they form a unique fingerprint.

Cite this