scholarly journals A Forest Fire Detection System Based on Ensemble Learning

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 217
Author(s):  
Renjie Xu ◽  
Haifeng Lin ◽  
Kangjie Lu ◽  
Lin Cao ◽  
Yunfei Liu
Keyword(s):  
Ensemble Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Ground Truth ◽  
False Positives ◽  
Learning Technology ◽  
Global Information ◽  
Forest Fire Detection

Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.

scholarly journals Forest Fire Detection System using Zig Bee

2020 ◽  
Vol 9 (6) ◽  
pp. 15-17
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Disaster Response ◽  
Detection System ◽  
Environmental Parameters ◽  
Fire Detection ◽  
Fire Departments ◽  
Average Temperature ◽  
Response Capacity ◽  
Forest Fire Detection

The forest fires are alone not a threat to the environment but this add on multiple factors which are threat to the environment few examples are decreasing of ground water level, landslides, global warming. These are dangerous for the animal species and few vulnerable species. In recent years few a case has been heard of forest fires in Amazon, California which damaged the flora and fauna. Scientist have also predicted that by end of twentieth century the average temperature of earth will rise by 4.4 degree Celsius. The forest fire detection will give strength to the disaster response capacity. Environmental parameters such as forest temperature and humidity can be tracked in real time. From the information gathered by the system, wildfire fighting or fire prevention decisions can be taken as quickly as possible by the respective fire departments.

scholarly journals Forest Fire Detection System

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1138-1142
Keyword(s):  
Real Time ◽  
Forest Fire ◽  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Wildlife Habitat ◽  
Support Vector ◽  
Time Data ◽  
Temporal Segmentation ◽  
Forest Fire Detection

The forest is one of the most important wealth of every country. The forest fires destroys the wildlife habitat, damages the environment, affects the climate, spoils the biological properties of the soil, etc. So the forest fire detection is a major issue in the present decade. At the same time the forest fire have to be detected as fast as possible. In the proposed method, a color spatial segmentation, temporal segmentation, global motion compensation, Support Vector Machine (SVM) classifications are used to detect the fire and to segment the fire from the video sequence. The method is implemented over the two real time data sets. The proposed method is most suitable for segmenting fire events over unconstrained videos in real time.

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.

scholarly journals Fire detection and notification using a Zigbee wireless sensor network

2021 ◽  
Vol I (I) ◽  
Author(s):  
Priyadharshini S
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Forest Fire ◽  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Wireless Sensor ◽  
And Control ◽  
Forest Conditions

Forest fires are the most common threat in the woods. A combination of natural and human-made factors contributes to forest fires. Forest fires destroy trees, which are essential to produce oxygen, which we need to live. This new Zigbee-based wireless sensor network is being developed to overcome the limitations of existing technologies like the MODIS satellite-based detection system and a basic wireless sensor network. It's difficult to contain a forest fire that wasn't predicted or noticed in time. As a result, it's critical to catch a wildfire early enough before it spreads too far. Using a GSM device, the proposed method would gather data on forest conditions such as temperature, humidity, smoke, and flames, and deliver it to the appropriate authorities. There are three parts to the project's concept. Modules for sensors, gateways, and control centres make up the three sections. This project's main objective is to benefit others.

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