• Journal of the Korean Society of Safety
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• v.36 no.2
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• pp.87-93
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• 2021

Modern society shows rapid growth that is different from that of the development of existing technologies. The development of these technologies has led to the tendency of buildings to become dense, large and advancing. Regarding fire hazards, the possibility of large-scale fires causing fatal damage, due to the rapid spread of fire, increases. Therefore, for this reason, fire defense, i.e. detection and fire extinguishing facilities, in buildings are essential and well applied. But there are always limitations to that. Based on this reason, we would like to suggest the introduction of a new concept of a fire safety system. The method presented here is not only to use a single system for fire detection and fire extinguishing systems but to jointly use it in the environment and energy management fields within the building. However, an important step is required before introducing a system of these technologies. The fire extinguishing method proposed by this system is a method of extinguishing by blocking oxygen flowing into the space where the fire occurred. However, a sufficient basis is needed for this system to be applied in practice. Therefore, in this study, we intend to conduct a preliminary experiment to introduce the new concept of fire detection and extinguishing. The experiment used ethanol with a relatively simple combustion reaction and a high possibility of complete combustion. As a result, it was confirmed how the internal values changed during a fire using ethanol. Resultingly, we obtained the internal oxygen concentration and internal environmental changes according to the initial flame size. Lastly, the data accumulated in this study can be used as data for application in an automatic fire extinguishing system.

• Journal of Satellite, Information and Communications
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• v.8 no.3
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• pp.52-57
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• 2013

This paper describes the color comparison analysis of flame in each standard color model in order to propose the optimal color model for image processing based flame detection algorithm. Histogram intersection values were used to analyze the separation characteristics between color of flame and color of non-flame in each standard color model which are RGB, YCbCr, CIE Lab, HSV. Histogram intersection value in each color model and components is evaluated for objective comparison. The analyzed result shows that YCbCr color model is the most suitable for flame detection by average HI value of 0.0575. Among the 12 components of standard color models, each Cb, R, Cr component has respectively HI value of 0.0433, 0.0526, 0.0567 and they have shown the best flame separation characteristics.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.516-521
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• 2010

In this paper, we propose a video-based intelligent unmanned fire surveillance system using fuzzy color models. In general, to detect heat or smoke, a separate device is required for a fire surveillance system, this system, however, can be implemented by using widely used CCTV, which does not need separate devices and extra cost. The systems called video-based fire surveillance systems use mainly a method extracting smoke or flame from an input image only. The smoke is difficult to extract at night because of its gray-scale color, and the flame color depends on the temperature, the inflammable, the size of flame, etc, which makes it hard to extract the flame region from the input image. This paper deals with a intelligent fire surveillance system which is robust against the variation of the flame color, especially at night. The proposed system extracts the moving object from the input image, makes a decision whether the object is the flame or not by means of the color obtained by fuzzy color model and the shape obtained by histogram, and issues a fire alarm when the flame is spread. Finally, we verify the efficiency of the proposed system through the experiment of the controlled real fire.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.1011-1016
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• 2009

In this paper, we propose the method of fire detection. A wide distribution of CCTV cameras (Closed Circuit Television) in many public areas can be used not only for video surveillance systems but also for detecting fire occurrence. A proposed approach is based on visual information through a static camera. Video sequences are analyzed to find fire candidates and then spatial analyses procedure for detected fire-like color foreground is carried out. From the simulation results, our method showed the best performance when spatial and temporal fire candidates changes rapidly and close to fire motion.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.590-598
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• 2017

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multichannel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.275-277
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• 2018

Recently, the importance of an early response has been emphasized due to the large fire. The most efficient method of extinguishing a large fire is early response to a small flame. To implement this solution, we propose a fire detection mechanism based on a deep learning artificial intelligence. In this study, a small amount of data sets is manipulated by an image augmentation technique using rotating, tilting, blurring, and distorting effects in order to increase the number of the data sets by 5 times, and we study the flame detection algorithm using faster R-CNN.

• Fire Science and Engineering
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• v.26 no.6
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• pp.24-30
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• 2012

This study is on the method of the detection for flame and series arc which can be happened at poor contact point added a vibration in part of contact point of low voltage line. In general, the causes of electric fire are over current, short circuit, poor contact, ect. The over-current or short circuit among those causes is detected by measuring a instant current value, but poor contact is difficult to detect by measuring a excessive value of the voltage and current and a distortion of waveforms. And therefore, in this paper, it is studied on the optimal technique of the arc judgement using fuzzy logic and MDET (Multi Dimension Estimation Technique). And it carries out the simulation for arc detection and the experiment for controller and load test. In result, the controller and detection algoristhm, is classified with normal wave and abnormal arc wave without relation with each loads and so the controller can detect a series arc successfully.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.284-286
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• 2012

There has been increasing attention about the prevention and counter-measure of disasters. Particularly, for the case of fire disaster, early detection reduces the damage caused by fire significantly and effective detection method is important. Since most existing detectors need to be located at a close distance to fire, analyzing camera images to find fire becomes active research topic. In this paper, we analyze the color characteristics of fire images in various color spaces and report the experimental detection results. The best result is 77.8% success rate in YIQ space.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.45-51
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• 2013

In this paper, the combination of ultraviolet and infrared sensors based design for flame signal detection algorithms was proposed with the application of light-wavelength from burning. And, the performance result of image detection was compared by an ultraviolet sensor, an infrared sensor, and the proposed dual-mode sensors(combination of ultraviolet and infrared sensors).

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.607-614
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• 2016

In this paper, we propose an intelligent fire learning and detection system using convolutional neural networks (CNN). Through the convolutional layer of the CNN, various features of flame and smoke images are automatically extracted, and these extracted features are learned to classify them into flame or smoke or no fire. In order to detect fire in the image, candidate fire regions are first extracted from the image and extracted candidate regions are passed through CNN. Experimental results on various image shows that our system has better performances over previous work.