Computer Vision Method for Forest Fires Detection Based on RGB Images Obtained by Unmanned Motor Glider

2021 ◽  
pp. 71-78
Author(s):  
Michael Yu. Kataev ◽  
Eugene Yu. Kartashov
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Monitoring Systems ◽  
Colour Space ◽  
Fire Monitoring ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Forest Fire Detection

The article proposes a method (algorithm) of forest fire detection by means of RGB images obtained by using an unmanned aerial vehicle (motor glider). It includes several stages associated with background detection and subtraction and recognition of fire areas by means of RGB colour space. The proposed method was tested using images of forest fires. It is proposed to use unmanned aerial vehicles capable to monitor large areas continuously for several hours. The results of calculations are shown, which demonstrate that the proposed method allows us to detect areas of images occupied by forest fires and may be used in automatic forest fire monitoring systems.

A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

2015 ◽  
Vol 45 (7) ◽  
pp. 783-792 ◽  
Author(s):  
Chi Yuan ◽  
Youmin Zhang ◽  
Zhixiang Liu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Forest Fire ◽  
Forest Fires ◽  
Cooperative Control ◽  
Fire Detection ◽  
Fire Monitoring ◽  
Diagnosis And Prognosis ◽  
Aerial Vehicles ◽  
Remote Sensing Techniques ◽  
Operational Range

Because of their rapid maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring, detecting, and fighting forest fires. Over the last decade, UAV-based forest fire fighting technology has shown increasing promise. This paper presents a systematic overview of current progress in this field. First, a brief review of the development and system architecture of UAV systems for forest fire monitoring, detection, and fighting is provided. Next, technologies related to UAV forest fire monitoring, detection, and fighting are briefly reviewed, including those associated with fire detection, diagnosis, and prognosis, image vibration elimination, and cooperative control of UAVs. The final section outlines existing challenges and potential solutions in the application of UAVs to forest firefighting.

Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires

2020 ◽  
Vol 149 ◽  
pp. 1-16 ◽  
Author(s):  
S. Sudhakar ◽  
V. Vijayakumar ◽  
C. Sathiya Kumar ◽  
V. Priya ◽  
Logesh Ravi ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
False Alarms ◽  
Aerial Vehicle ◽  
Forest Fire Detection

scholarly journals A Spatiotemporal Contextual Model for Forest Fire Detection Using Himawari-8 Satellite Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1992 ◽  
Author(s):  
Zixi Xie ◽  
Weiguo Song ◽  
Rui Ba ◽  
Xiaolian Li ◽  
Long Xia
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Forest Fire ◽  
Forest Fires ◽  
Spatial Information ◽  
Fire Detection ◽  
High Temporal Resolution ◽  
Detection Accuracy ◽  
Contextual Model ◽  
Forest Fire Detection

Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest fire detection algorithms that are based on a single one of these two systems have only exploited temporal or spatial information independently. There are no approaches yet that have effectively merged spatial and temporal characteristics to detect forest fires. This paper fills this gap by presenting a spatiotemporal contextual model (STCM) that fully exploits geostationary data’s spatial and temporal dimensions based on the data from Himawari-8 Satellite. We used an improved robust fitting algorithm to model each pixel’s diurnal temperature cycles (DTC) in the middle and long infrared bands. For each pixel, a Kalman filter was used to blend the DTC to estimate the true background brightness temperature. Subsequently, we utilized the Otsu method to identify the fire after using an MVC (maximum value month composite of NDVI) threshold to test which areas have enough fuel to support such events. Finally, we used a continuous timeslot test to correct the fire detection results. The proposed algorithm was applied to four fire cases in East Asia and Australia in 2016. A comparison of detection results between MODIS Terra and Aqua active fire products (MOD14 and MYD14) demonstrated that the proposed algorithm from this paper effectively analyzed the spatiotemporal information contained in multi-temporal remotely sensed data. In addition, this new forest fire detection method can lead to higher detection accuracy than the traditional contextual and temporal algorithms. By developing algorithms that are based on AHI measurements to meet the requirement to detect forest fires promptly and accurately, this paper assists both emergency responders and the general public to mitigate the damage of forest fires.

scholarly journals An Iot Based Forest Fire Detection using Raspberry Pi

2019 ◽  
Vol 8 (4) ◽  
pp. 9126-9132
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Early Stage ◽  
Fire Detection ◽  
Base Station ◽  
Raspberry Pi ◽  
Alert Message ◽  
Forest Fire Detection ◽  
Monitoring Stations

As we all know forests are the main source of oxygen and its protection is essential to sustain the human and animal race. Since we all learnt about the necessity of air, yet we lack at taking measures to protect our mother forest. Forest Fires are the main reason for the deforestation and destruction of trees and wildlife. Forest Fires are due to these two ways either by man-made or naturally caused. In either way we have to pay for the loss occurred because we have left with only certain area for the forest. So, we have to take measures to prevent forest fire at its early stage. The main aim of our project is to design and implement an IoT based hardware module that could detect the fire and prevent it by alerting the monitoring stations with an alert message and also provides location to the nearest base station. An automatic message will be sent to the nearest base station in addition to these, it has a 360 degrees rotation camera which helps to provide continuous surveillance. We can rotate the camera in any direction from the base station itself. A buzzer that alarms when the incident is happening and a water motor, this water motor will be on automatically. We can also find location where the incident is taking place with the help of Wi-Fi module. This device helps in identifying the fire at its early stage and helps in the prevention of spread all over the forest.

Forest fire flame and smoke detection from UAV-captured images using fire-specific color features and multi-color space local binary pattern

2020 ◽  
Vol 8 (4) ◽  
pp. 285-309
Author(s):  
F.M. Anim Hossain ◽  
Youmin M. Zhang ◽  
Masuda Akter Tonima
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Forest Fire ◽  
Local Binary Pattern ◽  
Color Space ◽  
Fire Detection ◽  
Large Field ◽  
Aerial Vehicles ◽  
Aerial Platforms ◽  
Forest Fire Detection

In recent years, the frequency and severity of forest fire occurrence have increased, compelling the research communities to actively search for early forest fire detection and suppression methods. Remote sensing using computer vision techniques can provide early detection from a large field of view along with providing additional information such as location and severity of the fire. Over the last few years, the feasibility of forest fire detection by combining computer vision and aerial platforms such as manned and unmanned aerial vehicles, especially low cost and small-size unmanned aerial vehicles, have been experimented with and have shown promise by providing detection, geolocation, and fire characteristic information. This paper adds to the existing research by proposing a novel method of detecting forest fire using color and multi-color space local binary pattern of both flame and smoke signatures and a single artificial neural network. The training and evaluation images in this paper have been mostly obtained from aerial platforms with challenging circumstances such as minuscule flame pixels, varying illumination and range, complex backgrounds, occluded flame and smoke regions, and smoke blending into the background. The proposed method has achieved F1 scores of 0.84 for flame and 0.90 for smoke while maintaining a processing speed of 19 frames per second. It has outperformed support vector machine, random forest, Bayesian classifiers and YOLOv3, and has demonstrated the capability of detecting challenging flame and smoke regions of a wide range of sizes, colors, textures, and opacity.

scholarly journals IMPROVEMENT OF FOREST FIRE DETECTION ALGORITHM USING BRIGHTNESS TEMPERATURE LAPSE RATE CORRECTION IN HIMAWARI-8 IR CHANNELS: APPLICATION TO THE 6 MAY 2017 SAMCHEOK CITY, KOREA

2018 ◽  
Vol XLII-3 ◽  
pp. 1353-1354
Author(s):  
S. H. Park ◽  
W. Park ◽  
H. S. Jung
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Continuous Monitoring ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Time Of Day ◽  
Lapse Rate ◽  
Mountainous Areas ◽  
Temperature Lapse Rate ◽  
Forest Fire Detection

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.

scholarly journals Automatic Forest Fire Detection and Suppression Smart Grid System

2021 ◽  
pp. 666-669
Author(s):  
Gopalakrishnan G ◽  
Arul Mozhi Varman S ◽  
Dinessh T C ◽  
Divayarupa S ◽  
Benazir Begam R
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Radical Change ◽  
Climatic Conditions ◽  
Grid System ◽  
The Past ◽  
Initial Stage ◽  
Smart Grid System ◽  
Forest Fire Detection

Over the past years, a radical change in Earth’s temperature has been recorded. It has caused global warming and severe changes in climatic conditions. Naturally, this has resulted in many natural disasters. Forest fire is one such calamity that harms the environment to a great extent. The traditional methods of controlling and suppressing the fires are ineffective as the fires spread too rapidly if it is not contained at the initial stage. Hence this paper proposes a system that aims to automatically detect forest fires and suppress them. This system will suppress and contain the forest fires long enough for the firefighters to arrive.

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