Fire detection system using random forest classification for image sequences of complex background

Kim, Onecue; Kang, Dong-Joong

doi:10.1117/1.oe.52.6.067202

Cited by 21 publications

(7 citation statements)

References 14 publications

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“…By contrast, RF is a non-parametric machine learning ensemble algorithm that has a strong anti-noise ability and low sensitivity to outliers and can effectively overcome the over fitting phenomenon [13]. Based on these advantages, RF has achieved high classification accuracy in research, and it is gradually becoming widely used in the remote sensing image classification field [14].…”

Section: Introductionmentioning

confidence: 99%

Wildfires on the Mongolian Plateau: Identifying Drivers and Spatial Distributions to Predict Wildfire Probability

et al. 2019

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With climate change, significant fluctuations in wildfires have been observed on the Mongolian Plateau. The ability to predict the distribution of wildfires in the context of climate change plays a critical role in wildfire management and ecosystem maintenance. In this paper, Ripley’s K function and a Random Forest (RF) model were applied to analyse the spatial patterns and main influencing factors affecting the occurrence of wildfire on the Mongolian Plateau. The results showed that the wildfires were mainly clustered in space due to the combination of influencing factors. The distance scale is less than 1/2 of the length of the Mongolian Plateau; that is, it does not experience boundary effects in the study area and it meets the requirements of Ripley’s K function. Among the driving factors, the fraction of vegetation coverage (FVC), land use degree (La), elevation, precipitation (pre), wet day frequency (wet), and maximum temperature (tmx) had the greatest influences, while the aspect had the lowest influence. The likelihood of fire was mainly concentrated in the northern, eastern, and southern parts of the Mongolian Plateau and in the border area between the Inner Mongolia Autonomous Region (Inner Mongolia) and Mongolian People’s Republic (Mongolia), and wildfires did not occur or occurred less frequently in the hinterland area. The fitting results of the RF model showed a prediction accuracy exceeding 90%, which indicates that the model has a high ability to predict wildfire occurrences on the Mongolian Plateau. This study can provide a reference for predictions and decision-making related to wildfires on the Mongolian Plateau.

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Section: Introductionmentioning

confidence: 99%

Wildfires on the Mongolian Plateau: Identifying Drivers and Spatial Distributions to Predict Wildfire Probability

et al. 2019

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“…False alarms were also produced due to the threshold‐based forest fire detection technique. Kim et al 12 discussed about the fire alarm system in which embedded surveillance machine is designed to monitor the fire detection in outside atmosphere. Whenever, the local features such as color, motion, mean and variation are to be determined and also blinking information is designed by means of using cross patches in order to minimize the false detection.…”

Section: Related Workmentioning

confidence: 99%

Forest fire detection on LANDSAT images using support vector machine

Chanthiya¹,

Kalaivani²

2021

Concurrency and Computation

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In recent days, the major threat in the world is forest fire that affects the biodiversity, climate change, and so forth. So detection process is more essential to monitor the forest region. To detect the forest fire, the paper proposes a novel detection technique of support vector machine (SVM)-Krill herd that can effectively detect the fire region using different kinds of features. Land surface temperature, fire intensity, water vapor, and top of atmosphere temperature are being extracted as some of the features that can be exposed. These features are preferred to classify the LANDSAT image into two classes using SVM optimized by Krill herd. The Euclidean distance is chosen to find the similarity between the test and trained image and then predict the giving query image containing a fire or not based on its training samples. With the help of the feature extractor parameters, the performances have to be analyzed. When compared with existing fire detection algorithms like active fire detection, SFIDE, convolutional neural network (CNN), hybrid intelligent, and PSO-SVM algorithm, the proposed SVM-Krill herd-based detection method increases the accuracy by 1.562%, 0.675%, 1.290%, 0.876%, and 1.038%. The proposed detection method of SVM-Krill herd achieves 99.21% accuracy and high precision as 98.41%.

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“…Embedded machine learning is casually adopted in smartphones ( [6,7]), CCTV cameras (e.g., Reference [8]) and robots (e.g., Reference [9]). The concept of hierarchical classification itself has been proposed for sound classification in the context of smartphones [10] and smart vehicles [11].…”

Section: Related Workmentioning

confidence: 99%

From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

Zalewski

Marchegiani

Elsts

et al. 2020

Sensors

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Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.

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Fire detection system using random forest classification for image sequences of complex background

Cited by 21 publications

References 14 publications

Wildfires on the Mongolian Plateau: Identifying Drivers and Spatial Distributions to Predict Wildfire Probability

Wildfires on the Mongolian Plateau: Identifying Drivers and Spatial Distributions to Predict Wildfire Probability

Forest fire detection on LANDSAT images using support vector machine

From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

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