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

Fire Detection Using Both Infrared and Visual Images With Application to Unmanned Aerial Vehicle Forest Fire Surveillance

2019 ◽  
Author(s):  
Chi Yuan ◽  
Zhixiang Liu ◽  
Anim Hossain ◽  
Youmin Zhang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Forest Fire ◽  
Forest Fires ◽  
Low Cost ◽  
Fire Detection ◽  
Visual Images ◽  
Camera System ◽  
Real Time Processing ◽  
Computational Performance ◽  
Aerial Vehicle

Abstract Forest fires are a universal problem that destroy a large amount of natural resources and creates environmental pollution. Forest firefighting is one of today’s most important events for natural and environmental resources protection and conservation. Unmanned aerial vehicle (UAV) with remote sensing system can offer a rapid, safe and low-cost approach for effective forest fire detection which have attracted researchers attention worldwide. In this paper, automatic detection of fire regions using both visual and infrared images is investigated. In order to improve the computational performance to satisfy the requirement of real-time processing, a reduced complexity fusion method is adopted in this research. Through testing the proposed approach on real video sequences, good detection performance is achieved and it is indicated that using multi-modal camera system to detect forest fire with application to firefighting UAV is very promising.

scholarly journals Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

2019 ◽  
Vol 11 (3) ◽  
pp. 271 ◽  
Author(s):  
Eunna Jang ◽  
Yoojin Kang ◽  
Jungho Im ◽  
Dong-Won Lee ◽  
Jongmin Yoon ◽  
...  
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Forest Fire ◽  
Satellite Data ◽  
Forest Fires ◽  
Fire Detection ◽  
Geostationary Satellite ◽  
False Alarms ◽  
Post Processing ◽  
Forest Fire Detection

Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50–60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.

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.

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.

Field Experiment Testbed for Forest Fire Detection using Wireless Multimedia Sensor Network

2020 ◽  
Vol 10 (1) ◽  
pp. 3-14
Author(s):  
Houache Noureddine ◽  
Kechar Bouabdellah
Keyword(s):  
Field Experiment ◽  
Forest Fire ◽  
Field Experiments ◽  
Detection System ◽  
Low Cost ◽  
Fire Detection ◽  
False Alarms ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Forest Fire Detection

Forest fire disasters have arisen each year due to a number of factors. The main interest of the authorities is to fight against these fires as early as possible with a minimum of damage, by exploiting recent technologies suitable for this field. In this paper, we present the design and the implementation of a forest fire detection system based on the Wireless Multimedia Sensor Networks (WMSN) technology applied to our region (M'sila forest, Oran city - Algeria) using a field experiment testbed with low cost hardware and software. In our previous study, the designed system detects the fire using a mono modal approach (the sensed data was scalar in nature such as the temperature and humidity). In this work, we enhanced this system by collecting, in addition, richer information sources using cameras as data sources (by capturing images) to eliminate the false alarms which present the main weakness of the first system. We call this new system as Multimedia Forest Fire System (M2FS). Field experiments that we have carried out using the testbed under different scenarios by evaluating the image compression, time constraint and energy consumption, allowed us to validate our chosen technology (Arduino mote) for any application (scalar or multimedia), and also revealed the supremacy of the multimodal approach to mitigate efficiently false alarms.

scholarly journals Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures

2020 ◽  
Vol 12 (19) ◽  
pp. 3177 ◽  
Author(s):  
Panagiotis Barmpoutis ◽  
Tania Stathaki ◽  
Kosmas Dimitropoulos ◽  
Nikos Grammalidis
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Forest Fires ◽  
Large Scale ◽  
Computational Cost ◽  
Fire Detection ◽  
Adaptive Method ◽  
Field Of View ◽  
Aerial Vehicle ◽  
Remote Sensing Systems ◽  
Early Fire Detection

The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire detection, making it possible to obtain unlimited field of view captures which reduce the number of required sensors and the computational cost and make the systems more efficient. More specifically, once optical 360-degree raw data are obtained using an RGB 360-degree camera mounted on an unmanned aerial vehicle, we convert the equirectangular projection format images to stereographic images. Then, two DeepLab V3+ networks are applied to perform flame and smoke segmentation, respectively. Subsequently, a novel post-validation adaptive method is proposed exploiting the environmental appearance of each test image and reducing the false-positive rates. For evaluating the performance of the proposed system, a dataset, namely the “Fire detection 360-degree dataset”, consisting of 150 unlimited field of view images that contain both synthetic and real fire, was created. Experimental results demonstrate the great potential of the proposed system, which has achieved an F-score fire detection rate equal to 94.6%, hence reducing the number of required sensors. This indicates that the proposed method could significantly contribute to early fire detection.

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