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 Pedersen; Dimitrios Chrysostomou

doi:10.1109/SII46433.2020.9026244

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 Pedersen, Dimitrios Chrysostomou

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

11 Citations (Scopus)

165 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 language	English
Title of host publication	IEEE/SICE International Symposium on System Integration
Number of pages	6
Publisher	IEEE
Publication date	9 Mar 2020
Pages	867-872
Article number	9026244
ISBN (Print)	78-1-7281-6668-1
ISBN (Electronic)	978-1-7281-6667-4
DOIs	https://doi.org/10.1109/SII46433.2020.9026244
Publication status	Published - 9 Mar 2020
Event	IEEE/SICE International Symposium on System Integration - Hawaii Convention Center, Honolulu, United States Duration: 12 Jan 2020 → 15 Jan 2020

Conference

Conference	IEEE/SICE International Symposium on System Integration
Location	Hawaii Convention Center
Country/Territory	United States
City	Honolulu
Period	12/01/2020 → 15/01/2020

Series	Proceedings of the 2020 IEEE/SICE International Symposium on System Integration
ISSN	2474-2325

Keywords

DCNN
quadcopter navigation
wildfire inspection
drone control
deep convolutional neural networks

Access to Document

10.1109/SII46433.2020.9026244

SII20_0001_FIAccepted author manuscript, 1.16 MBLicence: CC BY-NC-ND 4.0

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Cite this

@inproceedings{f9af3e11d0ba4802b7fbebff9908be17,

title = "A Framework for Wildfire Inspection Using Deep Convolutional Neural Networks",

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%.",

keywords = "DCNN, quadcopter navigation, wildfire inspection, drone control, deep convolutional neural networks",

author = "Iuliu Novac and Geipel, {Kenneth Richard} and Gil, {Jacobo Eduardo de Domingo} and Paula, {Lucas Goncalves de} and Pedersen, {Kristian Hyttel} and Dimitrios Chrysostomou",

year = "2020",

month = mar,

day = "9",

doi = "10.1109/SII46433.2020.9026244",

language = "English",

isbn = "78-1-7281-6668-1",

series = "Proceedings of the 2020 IEEE/SICE International Symposium on System Integration",

pages = "867--872",

booktitle = "IEEE/SICE International Symposium on System Integration",

publisher = "IEEE",

address = "United States",

note = "IEEE/SICE International Symposium on System Integration ; Conference date: 12-01-2020 Through 15-01-2020",

}

Novac, I, Geipel, KR, Gil, JEDD, Paula, LGD, Pedersen, KH & Chrysostomou, D 2020, A Framework for Wildfire Inspection Using Deep Convolutional Neural Networks. in IEEE/SICE International Symposium on System Integration., 9026244, IEEE, Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, pp. 867-872, IEEE/SICE International Symposium on System Integration, Honolulu, Hawaii, United States, 12/01/2020. https://doi.org/10.1109/SII46433.2020.9026244

A Framework for Wildfire Inspection Using Deep Convolutional Neural Networks. / Novac, Iuliu; Geipel, Kenneth Richard; Gil, Jacobo Eduardo de Domingo et al.
IEEE/SICE International Symposium on System Integration. IEEE, 2020. p. 867-872 9026244 (Proceedings of the 2020 IEEE/SICE International Symposium on System Integration).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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T1 - A Framework for Wildfire Inspection Using Deep Convolutional Neural Networks

AU - Novac, Iuliu

AU - Geipel, Kenneth Richard

AU - Gil, Jacobo Eduardo de Domingo

AU - Paula, Lucas Goncalves de

AU - Pedersen, Kristian Hyttel

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PY - 2020/3/9

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N2 - 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%.

AB - 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%.

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U2 - 10.1109/SII46433.2020.9026244

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M3 - Article in proceeding

SN - 78-1-7281-6668-1

T3 - Proceedings of the 2020 IEEE/SICE International Symposium on System Integration

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A Framework for Wildfire Inspection Using Deep Convolutional Neural Networks

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Special Session on Deep Learning As a Mean for Enabling Self-Learning and Self-Optimizing Capabilities in Real-World Industrial Applications

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A Framework for Wildfire Inspection Using Deep Convolutional Neural Networks

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Special Session on Deep Learning As a Mean for Enabling Self-Learning and Self-Optimizing Capabilities in Real-World Industrial Applications

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