Computer Vision for Fire Detection on UAVs—From Software to Hardware
Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in ord...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/13/8/200 |
| _version_ | 1797523766488596480 |
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| author | Seraphim S. Moumgiakmas Gerasimos G. Samatas George A. Papakostas |
| author_facet | Seraphim S. Moumgiakmas Gerasimos G. Samatas George A. Papakostas |
| author_sort | Seraphim S. Moumgiakmas |
| collection | DOAJ |
| description | Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest. |
| first_indexed | 2024-03-10T08:47:43Z |
| format | Article |
| id | doaj.art-5e25fd3784f6448fa3f06531b5a835c0 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T08:47:43Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-5e25fd3784f6448fa3f06531b5a835c02023-11-22T07:44:18ZengMDPI AGFuture Internet1999-59032021-07-0113820010.3390/fi13080200Computer Vision for Fire Detection on UAVs—From Software to HardwareSeraphim S. Moumgiakmas0Gerasimos G. Samatas1George A. Papakostas2Computer Science Department, International Hellenic University, 65404 Kavala, GreeceComputer Science Department, International Hellenic University, 65404 Kavala, GreeceComputer Science Department, International Hellenic University, 65404 Kavala, GreeceFire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest.https://www.mdpi.com/1999-5903/13/8/200UAVComputer Visionfire detectionwildfiresmoke |
| spellingShingle | Seraphim S. Moumgiakmas Gerasimos G. Samatas George A. Papakostas Computer Vision for Fire Detection on UAVs—From Software to Hardware Future Internet UAV Computer Vision fire detection wildfire smoke |
| title | Computer Vision for Fire Detection on UAVs—From Software to Hardware |
| title_full | Computer Vision for Fire Detection on UAVs—From Software to Hardware |
| title_fullStr | Computer Vision for Fire Detection on UAVs—From Software to Hardware |
| title_full_unstemmed | Computer Vision for Fire Detection on UAVs—From Software to Hardware |
| title_short | Computer Vision for Fire Detection on UAVs—From Software to Hardware |
| title_sort | computer vision for fire detection on uavs from software to hardware |
| topic | UAV Computer Vision fire detection wildfire smoke |
| url | https://www.mdpi.com/1999-5903/13/8/200 |
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