Image based Fire Detection using Convolutional Neural Network

김영진,; 김은경,

doi:10.6109/jkiice.2016.20.9.1649

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…Figure 3 shows the architecture of a CNN model [8]. A CNN model uses two-dimensional images as input data, extracts the unique features of the input images using convolution operations, and classifies the objects of the input images using the extracted features [9]. A CNN model consists of one or more convolutional layers and fully connected layers.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Development of a Forest Fire Detection System Using a Drone-based Convolutional Neural Network Model

Lee¹,

Jeong²,

Jung³

2023

Int J Fire Sci Eng

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Considering forest fires cause environmental destruction, ecosystem collapse, and severe damage to human lives and nature, developing a real-time, accurate, and stable forest fire detection system has become a critical issue in modern society. In this study, a drone-based forest fire detection system was developed using a convolutional neural network (CNN) model. Real-time forest fire detection models were developed using the CNN-based MobileNet algorithm, and their fire detection performance was evaluated. The main research results indicated that errors decreased and accuracy tended to increase during the model training and validation process as training progressed. Moreover, the V1 model exhibited the highest validation accuracy of 0.9466 among the MobileNet V1, V2, and V3 models and showed the highest accuracy of 0.9667 in evaluating the new test dataset during the model evaluation process.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Development of a Forest Fire Detection System Using a Drone-based Convolutional Neural Network Model

Lee¹,

Jeong²,

Jung³

2023

Int J Fire Sci Eng

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“…There is an ML algorithm for analyzing data. ML algorithms can be divided into shallow learning, such as Support Vector Machine (SVM), decision trees, and K-Means, as well as deep learning, which uses neural network layers such as a Deep Neural Network (DNN) and CNN [30,31,32,33,34]. The use of a deep learning model for classification is more flexible than a shallow learning model in the real environment.…”

Section: Related Workmentioning

confidence: 99%

Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Park

Lee

Yun

et al. 2019

Sensors

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A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

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“…Kim proposed [3] an image-based fire detection method using CNN (Convolutional Neural Network). In this method, firstly we extract fire candidate region using color information from video frame input and then detect fire using trained CNN.…”

Section: Introductionmentioning

confidence: 99%

Fire Detection Based on Image Using MATLAB GUI Programme

Mu'afa,

Silalahi,

Julitama

et al. 2023

JEEICT

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<p class="Keywords">Computer vision-based fire detection systems overcome this limitation in that they do not identify flammability on a product-by-product basis. In this study, fire detection was carried out using the YCbCr, RGB, and HSV map approach. The offered system uses color segmentation as a component of fire detection analysis. These three colors space segments will then be extracted to determine the presence of fire in the image used. A rule which consists of five rules based on color space condition had been constructed for classification of a pixel classified as fire. If a pixel satisfies these five rules, the pixels belong to fire class.This paper consists of 6 steps, including image acquisition, image pre-processing, image segmentation, feature extraction, image classification, and GUI creation. GUI provides a visual interface that is intuitive and easy for the user to understand the proposed system. By using button and another visual elements, users can interact with the system efficiently. Based on the tests carried out, the proposed system can detect images of fire in dark and light conditions. Performance testing is done by collecting a set of fire images on the internet. Performance is judged based on how many errors are generated when detecting fire. Performance is categorized into five types, including very good, good, fair, poor, and very poor.</p>

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Image based Fire Detection using Convolutional Neural Network

Cited by 12 publications

References 4 publications

Development of a Forest Fire Detection System Using a Drone-based Convolutional Neural Network Model

Development of a Forest Fire Detection System Using a Drone-based Convolutional Neural Network Model

Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Fire Detection Based on Image Using MATLAB GUI Programme

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