research-article

Vision-Based Forest Fire Detection Using Machine Learning

Authors:

Sukuan Jin,

Xiaobo LuAuthors Info & Claims

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

Article No.: 122, Pages 1 - 6

https://doi.org/10.1145/3331453.3361659

Published: 22 October 2019 Publication History

Get Access

Abstract

Forest fire does great harm to the society. This paper studies the method of forest fire detection combining image processing and machine learning based on video sequences. The method consists of three parts: moving object detection, image feature extraction and classifier recognition. An optimal algorithm combination of the above three parts is found through comparative experiments. In this paper, the LBP feature is improved based on color information. Before the moving object detection, image segmentation step is added in a novel way to reduce the false positive rate. Experimental results show that the proposed method yields good performance.

References

[1]

Guangmeng G, Mei Z (2004). Using MODIS land surface temperature to evaluate forest fire risk of northeast China [J]. IEEE Geoscience and Remote sensing letters, 1(2), 98--100.

Crossref

Google Scholar

[2]

Li Z, Nadon S, Cihlar J (2000). Satellite-based detection of Canadian boreal forest fires: Development and application of the algorithm [J]. International Journal of Remote Sensing, 21(16), 3057--3069.

Crossref

Google Scholar

[3]

Nakau K, Fukuda M, Kushida K, et al. (2006). Forest fire detection based on MODIS satellite imagery, and Comparison of NOAA satellite imagery with fire fighters' Information[C]//IARC/JAXA Terrestrial Team Workshop,18--23.

Google Scholar

[4]

Yu L, Wang N, Meng X (2005). Real-time forest fire detection with wireless sensor networks[C]//Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, IEEE, 2, 1214--1217.

Google Scholar

[5]

Hefeeda M, Bagheri M (2007). Wireless sensor networks for early detection of forest fires[C] 2007 IEEE International Conference on Mobile Adhoc and Sensor Systems. IEEE, 1--6.

Google Scholar

[6]

Ko B C, Kwak J Y, Nam J Y (2012). Wildfire smoke detection using temporospatial features and random forest classifiers[J]. Optical Engineering, 51(1), 017208.

Crossref

Google Scholar

[7]

Ma L, Wu K, Zhu L (2010). Fire smoke detection in video images using Kalman filter and Gaussian mixture color model[C] 2010 International Conference on Artificial Intelligence and Computational Intelligence. IEEE, 1, 484--487.

Google Scholar

[8]

Kandil M, Salama M (2009). A new hybrid algorithm for fire vision recognition[C] IEEE EUROCON 2009. IEEE, 1460--1466.

Google Scholar

[9]

Vipin V (2012). Image processing based forest fire detection[J]. International Journal of Emerging Technology and Advanced Engineering, 2(2): 87--95.

Google Scholar

[10]

Lee B, Han D (2007). Real-time fire detection using camera sequence image in tunnel environment[C] International Conference on Intelligent Computing. Springer, Berlin, Heidelberg, 1209--1220.

Google Scholar

[11]

Barnich O, Van Droogenbroeck M (2010). ViBe: A universal background subtraction algorithm for video sequences [J]. IEEE Transactions on Image processing, 20(6), 1709--1724.

Digital Library

Google Scholar

[12]

Ojala T, Pietikäinen M, Mäenpää T (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 7, 971--987.

Digital Library

Google Scholar

Cited By

View all

Davis MShekaramiz M(2023)Desert/Forest Fire Detection Using Machine/Deep Learning TechniquesFire10.3390/fire61104186:11(418)Online publication date: 29-Oct-2023
https://doi.org/10.3390/fire6110418
Cunha EMacêdo DZanchettin C(2023)Improving small object detection with DETRAug2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191541(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191541
Zhou MLiu SLi J(2023)Multi-scale Forest Flame Detection Based on Improved and Optimized YOLOv5Fire Technology10.1007/s10694-023-01486-559:6(3689-3708)Online publication date: 16-Sep-2023
https://doi.org/10.1007/s10694-023-01486-5
Show More Cited By

Index Terms

Vision-Based Forest Fire Detection Using Machine Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

Recommendations

Forest Fire Detection Based on Improved YOLOv5
AIPR '21: Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

Traditional forest fire detection based on sensing devices has a small range, low sensitivity and high false alarm rate, while existing forest fire detection methods based on deep learning have poor identification effect and inaccurate identification of ...
Forest fire detection using spiking neural networks
CF '18: Proceedings of the 15th ACM International Conference on Computing Frontiers

Forest fires is one of the main causes of environmental degradation and its detection and forecasting is challenging. A novel method of forest fire detection based on spiking neural networks is proposed in this paper. Data obtained from controlled ...
Fire detection in video surveillance using superpixel-based region proposal and ESE-ShuffleNet
Abstract
This paper proposes a forest fire detection framework using superpixel-based suspicious fire region proposal and light-weight convolutional neural network. The proposed methodology contains two main steps. In suspicious fire region proposal, we ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

October 2019

942 pages

ISBN:9781450362948

DOI:10.1145/3331453

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zeshui Xu

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

the Priority Academic Program Development of Jiangsu Higher Education Institutions
Key Research and Development Program in Jiangsu Province
the National Natural Science Foundation of China

Conference

CSAE 2019

CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering

October 22 - 24, 2019

Sanya, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
272
Total Downloads

Downloads (Last 12 months)19
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Davis MShekaramiz M(2023)Desert/Forest Fire Detection Using Machine/Deep Learning TechniquesFire10.3390/fire61104186:11(418)Online publication date: 29-Oct-2023
https://doi.org/10.3390/fire6110418
Cunha EMacêdo DZanchettin C(2023)Improving small object detection with DETRAug2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191541(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191541
Zhou MLiu SLi J(2023)Multi-scale Forest Flame Detection Based on Improved and Optimized YOLOv5Fire Technology10.1007/s10694-023-01486-559:6(3689-3708)Online publication date: 16-Sep-2023
https://doi.org/10.1007/s10694-023-01486-5
Susmitha IRoseline J F(2022)Detection of forest fire using support vector machine in comparison with k - nearest neighbour to measure the accuracy, precision and recall2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)10.1109/ICAC3N56670.2022.10074520(668-673)Online publication date: 16-Dec-2022
https://doi.org/10.1109/ICAC3N56670.2022.10074520
Zhou MLi JLiu S(2022)Fire Detection Based on Improved-YOLOv5sArtificial Neural Networks and Machine Learning – ICANN 202210.1007/978-3-031-15937-4_8(88-100)Online publication date: 6-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-15937-4_8
Fonseca C. EMarcillo DJácome-Guerrero SGualotuña TCruz H(2021)Identifying Technological Alternatives Focused on Early Alert or Detection of Forest Fires: Results Derived from an Empirical StudyArtificial Intelligence, Computer and Software Engineering Advances10.1007/978-3-030-68080-0_27(354-368)Online publication date: 20-Mar-2021
https://doi.org/10.1007/978-3-030-68080-0_27
Wu SGuo CYang J(2020)Using PCAand one‐stage detectors for real‐time forest fire detectionThe Journal of Engineering10.1049/joe.2019.11452020:13(383-387)Online publication date: 9-Jul-2020
https://doi.org/10.1049/joe.2019.1145

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Forest Fire Detection Based on Improved YOLOv5

Forest fire detection using spiking neural networks

Fire detection in video surveillance using superpixel-based region proposal and ESE-ShuffleNet