A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms
Wildfire is one of the most critical natural disasters that threaten wildlands and forest resources. Traditional firefighting systems, which are based on ground crew inspection, have several limits and can expose firefighters’ lives to danger. Thus, remote sensing technologies have become one of the...
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| Published in | Signal processing Vol. 190; p. 108309 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.01.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0165-1684 1872-7557 |
| DOI | 10.1016/j.sigpro.2021.108309 |
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| Abstract | Wildfire is one of the most critical natural disasters that threaten wildlands and forest resources. Traditional firefighting systems, which are based on ground crew inspection, have several limits and can expose firefighters’ lives to danger. Thus, remote sensing technologies have become one of the most demanded strategies to fight against wildfires, especially UAV-based remote sensing technologies. They have been adopted to detect forest fires at their early stages, before becoming uncontrollable. Autonomous wildfire early detection from UAV-based visual data using different deep learning algorithms has attracted significant interest in the last few years. To this end, in this paper, we focused on wildfires detection at their early stages in forest and wildland areas, using deep learning-based computer vision algorithms to prevent and then reduce disastrous losses in terms of human lives and forest resources. |
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| AbstractList | Wildfire is one of the most critical natural disasters that threaten wildlands and forest resources. Traditional firefighting systems, which are based on ground crew inspection, have several limits and can expose firefighters’ lives to danger. Thus, remote sensing technologies have become one of the most demanded strategies to fight against wildfires, especially UAV-based remote sensing technologies. They have been adopted to detect forest fires at their early stages, before becoming uncontrollable. Autonomous wildfire early detection from UAV-based visual data using different deep learning algorithms has attracted significant interest in the last few years. To this end, in this paper, we focused on wildfires detection at their early stages in forest and wildland areas, using deep learning-based computer vision algorithms to prevent and then reduce disastrous losses in terms of human lives and forest resources. |
| ArticleNumber | 108309 |
| Author | Bouguettaya, Abdelmalek Kechida, Ahmed Zarzour, Hafed Taberkit, Amine Mohammed |
| Author_xml | – sequence: 1 givenname: Abdelmalek surname: Bouguettaya fullname: Bouguettaya, Abdelmalek email: a.bouguettaya@crti.dz organization: Research Centre in Industrial Technologies (CRTI), P.O.Box 64, Cheraga 16014 Algiers, Algeria – sequence: 2 givenname: Hafed surname: Zarzour fullname: Zarzour, Hafed email: hafed.zarzour@gmail.com organization: Department of Mathematics and Computer Science, Souk Ahras University, Souk-Ahras, 41000, Algeria – sequence: 3 givenname: Amine Mohammed surname: Taberkit fullname: Taberkit, Amine Mohammed email: a.taberkit@crti.dz organization: Research Centre in Industrial Technologies (CRTI), P.O.Box 64, Cheraga 16014 Algiers, Algeria – sequence: 4 givenname: Ahmed surname: Kechida fullname: Kechida, Ahmed email: a.kechida@crti.dz organization: Research Centre in Industrial Technologies (CRTI), P.O.Box 64, Cheraga 16014 Algiers, Algeria |
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| Keywords | Deep learning Computer vision Smoke detection system Unmanned aerial vehicle Wildfire detection system Aerial images processing |
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| SubjectTerms | Aerial images processing Computer vision Deep learning Smoke detection system Unmanned aerial vehicle Wildfire detection system |
| Title | A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms |
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