• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1301-1314
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• 2018

In this paper, we present a low-cost fire detection system using a thermal camera and a smartphone. The developed system collects thermal and RGB videos from the developed camera. To detect fire, candidate fire regions are extracted from videos obtained using a thermal camera. The block mean of variation of adjacent frames is measured to analyze the dynamic characteristics of the candidate fire regions. After analyzing the dynamic characteristics of regions of interest, a fire is determined by the candidate fire regions. In order to evaluate the performance of our system, we compared with a smoke detector, a heat detector, and a flame detector. In the experiments, our fire detection system showed the excellent performance in detecting fire with an overall accuracy rate of 97.8 %.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.57-65
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• 2010

In this paper, we propose a new real-time algorithm detecting the flame and smoke in digital CCTV images. Because the forest fire causes the enormous human life and damage of property, the early management according to the early sensing is very important. The proposed algorithm for monitoring forest fire is classified into the flame sensing and detection of smoke. The flame sensing algorithm detects a flame through the conditional test at YCbCr color model from the single frame. For the detection of smoke, firstly the background range is set by using differences between current picture and the average picture among the adjacent frames in the weighted value, and the pixels which get out of this range and have a gray-scale are detected in the smoke area. Because the proposed flame sensing algorithm is stronger than the existing algorithms in the change of the illuminance according to the quantity of sunshine, and the smoke detection algorithm senses the pixel of a gray-scale with the smoke considering the amount of change for unit time, the effective early forest fire detection is possible. The experimental results indicate that the proposed algorithm provides better performance than existing algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.411-414
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• 2009

Many victims and property damages are caused in fires every year. In this paper, intelligent fire-detection systems with embedded fire-detection algorithms for early fire detection and alarm is proposed to reduce fire damages by using image processing technique, high speed digital signal processor(DSP) technique, and information technique. The fire detection algorithms used for the proposed systems consist of flame and smoke detection algorithms. If flame or smoke is detected respectively, the corresponding alarm signal can be transferred to management computer. And if flame and smoke is detected simultaneously, the fire alarm signal shall be generated. Through several experiments in the physical environment, it is shown that the proposed system works well without malfunction.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.382-386
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• 2005

In this paper we introduce a fire-detection system which is robust to light sources and environment changing. We can decide the threshold values that classify the regions between a fire flame and light sources by analyzing them in RGB color space. The mean histogram difference technique make it possible to extract flame region more efficient because fire flame is continuously changing after it occurs. In order to detect flame region, this paper proposes to count fire pixels.

• Journal of the Korean Society of Safety
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• v.32 no.2
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• pp.38-45
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• 2017

This paper is an experimental study to find an optimal detection method for detecting fire early in a rack-type warehouse stored with goods. In this study, we constructed rack-type structure with the fourth floor of 13.5 m high and conducted fire experiments which were to measure flow of heat/smoke in rack-type structure and response time of fire detectors. The detectors used at experiments were fixed temperature type detectors, rate of rise detectors, photoelectric smoke detectors, air sampling smoke detectors and flame detectors. The used ignition sources are n-heptane fire for response of heat detection and cotton fire for response of smoke detection. The fixed temperature type detectors, rate of rise detectors and photoelectric detectors were installed to every rack level respectively. The results show that the rate of rise detector should be installed every 2 levels and photoelectric smoke detector should be installed every 4 levels for the early stage fire detection. Air sampling smoke detectors can detect fire early in response to control of sensitivity, but there is a problem in false alarm. The fixed temperature detector is not suitable for early stage fire detection in warehouse and flame detector not worked if flame is not visible, so it need to install combination with other detector.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.8-14
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• 2014

In this paper, we propose a new fire detection method using moving features and colors of flames in video sequences. It uses YCbCr color space to separate the luminance from the chrominance components more effectively than RGB color space. In the proposed method, moving regions of flames are detected by cumulating the difference of luminance between two consecutive images and generate candidate flame regions by using the color of flames. Finally, it decides whether the candidate flame regions are flames or not by using their temporal changes of the areas. Experimental results show that the proposed method performs better in segmenting fire regions compared with the conventional fire detection method in video sequences.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.476-481
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• 2019

Early detection of fire is an important measure for minimizing the loss of life and property damage. However, fire and smoke need to be simultaneously detected. In this context, numerous studies have been conducted on image-based fire detection. Conventional fire detection methods are compute-intensive and comprise several algorithms for extracting the flame and smoke characteristics. Hence, deep learning algorithms and convolution neural networks can be alternatively employed for fire detection. In this study, recorded image data of fire in a ship engine room were analyzed. The flame and smoke characteristics were extracted from the outer box, and the YOLO (You Only Look Once) convolutional neural network algorithm was subsequently employed for learning and testing. Experimental results were evaluated with respect to three attributes, namely detection rate, error rate, and accuracy. The respective values of detection rate, error rate, and accuracy are found to be 0.994, 0.011, and 0.998 for the flame, 0.978, 0.021, and 0.978 for the smoke, and the calculation time is found to be 0.009 s.

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.463-470
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• 2009

In this paper, we propose the advanced fire-flame detection algorithm using camera image for better performance than previous sensors-based systems which is limited on small area. Also, previous works using camera image were depend on a lot of heuristic thresholds or required an additional computation time. To solve these problems, we use statistical values and divide image into blocks to reduce the processing time. First, from the captured image, candidate flame regions are detected by a background model and fire colored models of the fire-flame. After the probability models are formed using the change of luminance, wavelet transform and the change of motion on time axis, they are used for membership function of fuzzy logic. Finally, the result function is made by the defuzzification, and the probability value of fire-flame is estimated. The proposed system has shown better performance when it compared to Toreyin's method which perform well among existing algorithms.

• Journal of the Korean Society of Safety
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• v.16 no.1
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• pp.1-8
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• 2001

All objects emit thermal radiation and this radiation is the basis of the techniques used to detect flames. The usual phenomena occurring in the initial stage of the fire are generally invisible products of a combustion and visible smoke. Liquid or gaseous materials do not undergo a smoldering stage so that fires develop very rapidly. Also, the heat generated by the initial flames is usually not sufficient to activate a heat detector. In this case the most effective criterion for automatic fire detection is the flame. According to the fire regulation of korea, the compulsory standard provided that a flame detector shall be installed in a place that the attachment height of detector is higher than 20 m, chemical plants, hangar, refinery, etc.. The results of the research and development are discriminated between a flame and other radiant emitters, developed a UV detector tube contains an inert gas which absorbs UV radiation, developed PZT pyroelectric element is based on the use of photovoltanic cell, developed IR band-pass filter that only allow a 4.3 $\mu\textrm{m}$ radiation wavelength to reach the sensors and developed UV-IR combination flame detector combined into a single detection device.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.41-45
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• 2011

This paper presents a fire detection criterion based on flame color, spatial and temporal characteristic analysis of color fire images. To propose the criterion, Firstly the fire candidate regions were selected by using analyzed Cr and Y threshold value, and then texture analysis of candidate regions was performed by using DCT. Finally variation of Y values of these regions was calculated for temporal analysis. The proposed fire detection criterion was simulated by using fifteen test images and practicality was verified.