A Forest Fire Detection Algorithm Based on Sea Computing and Visual Saliency

Forest fires are a major natural disaster that destroys a forest area and a natural environment. In order to minimize the damage caused by the forest fire, it is necessary to know the location and the time of day and continuous monitoring is required until fire is fully put out. We have tried to improve the forest fire detection algorithm by using a method to reduce the variability of surrounding pixels. We focused that forest areas of East Asia, part of the Himawari-8 AHI coverage, are mostly located in mountainous areas. The proposed method was applied to the forest fire detection in Samcheok city, Korea on May 6 to 10, 2017.

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Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation

Mathematical Problems in Engineering ◽

10.1155/2018/7612487 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Mubarak A. I. Mahmoud ◽

Honge Ren

Keyword(s):

Temporal Variation ◽

Forest Fire ◽

Forest Fires ◽

Color Space ◽

Fire Detection ◽

Detection Algorithm ◽

Image Processing Algorithm ◽

Data Set ◽

Detection Rates ◽

Forest Fire Detection

Forest fires represent a real threat to human lives, ecological systems, and infrastructure. Many commercial fire detection sensor systems exist, but all of them are difficult to apply at large open spaces like forests because of their response delay, necessary maintenance needed, high cost, and other problems. In this paper a forest fire detection algorithm is proposed, and it consists of the following stages. Firstly, background subtraction is applied to movement containing region detection. Secondly, converting the segmented moving regions from RGB to YCbCr color space and applying five fire detection rules for separating candidate fire pixels were undertaken. Finally, temporal variation is then employed to differentiate between fire and fire-color objects. The proposed method is tested using data set consisting of 6 videos collected from Internet. The final results show that the proposed method achieves up to 96.63% of true detection rates. These results indicate that the proposed method is accurate and can be used in automatic forest fire-alarm systems.

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SVM based forest fire detection using static and dynamic features

Computer Science and Information Systems ◽

10.2298/csis101012030z ◽

2011 ◽

Vol 8 (3) ◽

pp. 821-841 ◽

Cited By ~ 34

Author(s):

Jianhui Zhao ◽

Zhong Zhang ◽

Shizhong Han ◽

Chengzhang Qu ◽

Zhiyong Yuan ◽

...

Keyword(s):

Forest Fire ◽

Bright Light ◽

Fire Detection ◽

Gaussian Mixture ◽

Detection Algorithm ◽

Svm Classifier ◽

Final Decision ◽

Dynamic Features ◽

Novel Approach ◽

Forest Fire Detection

A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance.

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Aerial Forest Fire Detection and Monitoring Using a Small UAV

KnE Engineering ◽

10.18502/keg.v5i6.7038 ◽

2020 ◽

Cited By ~ 1

Author(s):

Joaquim Vasconcelos Reinolds de Sousa ◽

Pedro Vieira Gamboa

Keyword(s):

Remote Sensing ◽

Forest Fire ◽

Forest Fires ◽

Fire Detection ◽

Detection Algorithm ◽

The Body ◽

Fire Fighting ◽

Aerial Images ◽

North East ◽

Forest Fire Detection

In recent years, large patches of forest have been destroyed by fires, bringing tragic consequences for the environment and small settlements established around these regions. In this context, it is essential that fire fighting teams possess an increased situational awareness about the fire propagation, in order to promptly act in the extinguishing process. Recent advances in UAV technology allied with remote sensing and computer vision techniques show very promising UAVs applicability in forest fires detection and monitoring. Besides presenting lower operational costs, these vehicles are able to reach regions that are inaccessible or considered too dangerous for fire fighting crews operations. This paper describes the application of a real-time forest fire detection algorithm using aerial images captured by a video camera onboard an Unmanned Aerial Vehicle (UAV). The forest fire detection algorithm consists of a rule-based colour model that uses both RGB and YCbCr colour spaces to identify fire pixels. An intuitive targeting system was also developed, allowing the detection of multiple fires at the same time. Additionally, a fire geolocation algorithm was developed in order to estimate the fire location in terms of latitude (φ), longitude (λ) and altitude (h). The geolocation algorithm consists of applying two coordinates systems transformations between the body-fixed frame, North-East-Down frame (NED) and Earth-Centered, Earth Fixed (ECEF) frame. Flight tests were performed during a controlled burn in order to assess the fire detection algorithm performance. The algorithm was able to detect the fire with few false positive detections. Keywords: Aerial fire detection algorithm, Aerial fire monitoring, Forest fire, UAV, Remote sensing

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