• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.70.2-70.2
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• 2010

본 연구는 최근 발생한 숭례문 화재와 같은 목조문화재의 화재를 초기에 인지하고 확인하는 차원에서 고안된 시스템으로써 불꽃감지기와 같은 초기 화재 감지시스템의 오작동 여부를 확인하여 화재감지기의 오작동으로 인한 경제적 시간적인 손실을 예방하고 목조문화재를 화재로부터 보호하기 위한 시스템이다. 초기에 화재를 감지하는 불꽃감지기는 현재 목조문화재뿐만 아니라 다양한 곳에서 활용되고 있지만 감지기의 오작동 및 오류를 확인하는 시스템은 실제로 실효성 등의 문제로 인해 활용이 미비한 실정이다. 본 연구에서는 유비쿼터스 센서 네트워크(USN) 기술, 불꽃감지기, 이미지 센서, USN 기반 문화재 방재 응용사례, 오작동 확인시스템 구현 등에 대해서 살펴보고 유비쿼터스형 문화재 방재시스템을 제시해 본다.

• Fire Science and Engineering
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• v.14 no.2
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• pp.1-6
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• 2000

The radiant energy from a flaming fire of fuels containing carbonaceous material can be applied to fast growing fire. Raiant energy sensinsing technique applied detectors are ultimately effective when early detecting fire alarm system is required or the smoke and heat detectors can not be applied. This study investigated the characteristics of sun light, artificial light and flame radiation light and the foundation technique of flame detecting is established. Pyroelectric element proper for the characteristics of flame radiant energy developed and circuit stabilizing technique, electro-magnetic immunity technique, durable and reliable operating technique to circuits developed.

• Journal of IKEEE
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• v.23 no.1
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• pp.112-119
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• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.495-496
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• 2017

불꽃의 파장을 감지하여 화재를 감지하는 불꽃감지기는 유형에 따라 자외선식, 적외선식, 다중파장식, 복합식으로 나뉜다. 불꽃감지기의 단점으로는 다양한 오작동과 신속한 대처가 담보되지 못한다는 데 있다. 이를 해결하기 위해서 오작동에 대한 대비책과 스마트 정보통신을 활용한 연구개발이 필요하다.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.5
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• pp.49-54
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• 2010

The wooden cultural assets have the characteristics such as the fast spread of flame and leading to total destruction. Therefore, there is a need for a system for early countermeasure of recognized problem, along with the technological response for accurately recognizing the situation, for the prevention and early suppression of fire. To utilize such technology for detecting the situation through the latest ubiquitous technology and for a quick response to suppress fire, the ubiquitous sensor network (USN) technology, flame detector, image sensor, USN-based cultural asset disaster prevention management application case and malfunction identification system realization were examined in this study and the study result was presented focusing on the flame detector malfunction identification system for the ubiquitous-type cultural asset disaster prevention system.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.712-721
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• 2010

This paper proposes a new fire-flame detection method using probabilistic membership function of visual features and Fuzzy Finite Automata (FFA). First, moving regions are detected by analyzing the background subtraction and candidate flame regions then identified by applying flame color models. Since flame regions generally have continuous and an irregular pattern continuously, membership functions of variance of intensity, wavelet energy and motion orientation are generated and applied to FFA. Since FFA combines the capabilities of automata with fuzzy logic, it not only provides a systemic approach to handle uncertainty in computational systems, but also can handle continuous spaces. The proposed algorithm is successfully applied to various fire videos and shows a better detection performance when compared with other methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.2
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• pp.331-336
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• 2017

The need for early fire detection technology is increasing in order to prevent fire disasters. Sensor device detection for heat, smoke and fire is widely used to detect flame and smoke, but this system is limited by the factors of the sensor environment. To solve these problems, many image-based fire detection systems are being developed. In this paper, we implemented a system to detect fire and smoke from camera input images using a convolution neural network. Through the implemented system using the convolution neural network, a feature map is generated for the smoke image and the fire image, and learning for classifying the smoke and fire is performed on the generated feature map. Experimental results on various images show excellent effects for classifying smoke and fire.

• 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.

• Fire Science and Engineering
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• v.33 no.1
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• pp.85-91
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• 2019

This study evaluated the fire detection performance of an automatic fire extinguishing system for road tunnels, which combines flame wavelength detection technology with flame image detection technology. This fusion technique to improve the fire detection capability can reduce the damage caused by the fire suppression by locating the fire source in the fire and discharging the pressurized water only at the fire source. Experiments were conducted to determine the position of a fire source when a $70cm{\times}70cm$ target was placed at a distance of 15 m, 20 m, 25 m, 30 m, and 35 m, respectively, in a situation where there is a flame and smoke in a tunnel. The performance of the ultraviolet and triple wavelength infrared (IR3) sensors was attenuated due to the interference of thick smoke. In addition when the flame was blocked by thick smoke, the image sensor sensed the smoke and emitted a fire signal.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.293-294
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• 2022

본 프로젝트에서는 화재 발생 시 발견하기 어려운 곳이나 화재가 빈번한 곳에 구축하는 것을 목적으로 하고 있다. 가스와 불꽃을 감지하는 센서로 화재를 감지하고, 디스플레이와 LED 그리고 소리를 통해 화재발생을 알려준다. 그 후, 스프링클러가 작동하여 초기화재에 대응에 도움을 주고 119에 자동으로 신고가 된다. 일정 수치의 센서에 대한 감지 값을 인식하고 인식한 감지 값에 반응하여 화재 대처를 가능하게 구현하는 시스템을 제안한다. 감지센서를 통한 화재장소에서의 불꽃과 가스를 감지하게 되어 스프링클러가 1차적으로 화재의 번짐을 지연해 주고 은근 소방서에 자동적으로 신고를 하게 되는 자동화 프로그램 이행을 목표로 하고 있다.