• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.287-293
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• 2014

Early fire detection is very important to avoid loss of lives and material damage. The conventional smoke detector sensors have difficulties in detecting smoke in large outdoor areas. The video-based smoke detection can overcome these drawbacks. This paper proposes a new smoke detection method in video sequences. It uses the ratio of variation rate of subband energy in the wavelet transform domain. In order to reduce the false alarm, candidate smoke blocks are detected by using motion, decrease of chromaticity and the average intensity of block in the YUV color space. Finally, it decides whether the candidate smoke blocks are smokes or not by using their temporal changes of subband energies in the wavelet transform domain. Experimental results show that the proposed method noticeably increases the accuracy of smoke detection and reduces false alarm compared with the conventional smoke detection methods using wavelets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1649-1656
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• 2016

Performance of the existing sensor-based fire detection system is limited according to factors in the environment surrounding the sensor. A number of image-based fire detection systems were introduced in order to solve these problem. But such a system can generate a false alarm for objects similar in appearance to fire due to algorithm that directly defines the characteristics of a flame. Also fir detection systems using movement between video flames cannot operate correctly as intended in an environment in which the network is unstable. In this paper, we propose 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. Also, we show that the performance is significantly improved compared to the detection rate and missing rate found in previous studies.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.3-8
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• 2005

위성영상을 이용한 산불탐지기법은 단순한 화재의 유무뿐만 아니라 온도, 면적, 상태 등과 같은 다양한 정보를 얻을 수 있는 수준으로 개발되었다. 하지만 실제 적용결과에 있어서는 서로 다른 탐지결과를 보이기 때문에 어떤 탐지기법을 선택하고 기법에 사용되는 임계값들을 어떻게 결정해야 하는가라는 문제점이 발생한다. 본 연구에서는 의사결정기법을 이용하여 개별 탐지기법의 결과를 분석하여 최적해를 얻고자 하였다. 이를 위하여 국내에서 발생한 산불에 주요 탐지기법을 적용, 정확도를 평가한 후 의사결정기법을 적용하여 보다 정확한 결과를 얻는 연구를 수행하였다. 그 결과 단일 탐지기법에서 발생하는 오류(false alarm)을 일정부분 보완할 수 있었다. 하지만 충분한 실측자료가 부족하여 정확한 평가 및 검증에는 한계가 있었다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.06a
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• pp.286-286
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• 2010

In this paper, we proposed that optimum installation methods and characteristics of the hybrid type flame-sensor. The hybrid type flame-sensor has the high responsive performance. This research introduced the characteristics of the hybrid type flame-sensor, it was consist of IR/UV flame detector and proposed the optimum installation methods of false alarm reduced to resemble fire.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5522-5536
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• 2017

In this paper, a video based smoke detection method using dynamic texture feature extraction with volume local binary patterns is studied. Block based method was used to distinguish smoke frames in high definition videos obtained by experiments firstly. Then we propose a method that directly extracts dynamic texture features based on irregular motion regions to reduce adverse impacts of block size and motion area ratio threshold. Several general volume local binary patterns were used to extract dynamic texture, including LBPTOP, VLBP, CLBPTOP and CVLBP, to study the effect of the number of sample points, frame interval and modes of the operator on smoke detection. Support vector machine was used as the classifier for dynamic texture features. The results show that dynamic texture is a reliable clue for video based smoke detection. It is generally conducive to reducing the false alarm rate by increasing the dimension of the feature vector. However, it does not always contribute to the improvement of the detection rate. Additionally, it is found that the feature computing time is not directly related to the vector dimension in our experiments, which is important for the realization of real-time detection.

• Fire Science and Engineering
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• v.30 no.4
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• pp.1-5
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• 2016

This research performed tests of Video Fire Detector and criteria of installation to make suggestions regarding the criteria that must be reflected in NFSC 203 by comparing the standards of FM Approvals, UL, ISO7240 and NFPA 72. FM Standard related to Video Fire Detector test has been classified as Smoke, Flame type, but the UL Standard has classified only as a Smoke type. This research examined 6 cases of fire phenomenon detection case in ISO 7240 and 3 cases in NFPA 72, respectively. There are 15 items required for the installation standard of a Video Fire Detector and each field standard is presented as a per installation method. To apply a Video Fire Detector, the pertinent items (the definition of term, detector's classification, structure and function among its test item) must be inserted. In addition, 7 items of the fire test, i.e., the sensitivity adjustment, prevent false alarm, ambient temperature test, the effective sensitivity and detection distance and viewing angle, aging test, flood test, must be applied to the actual test. For installation in the field, the operation environment and levels of illumination, and NFSC 203 must be set, and standards relevant to the sound system, indicators' installation distance, etc. need to be inserted.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.427-430
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• 2007

Many victims and property damages are caused in fires every year. In this paper, flame and smoke detection algorithm by using image processing technique is proposed to early alarm fires. The first decision of proposed algorithms is to check candidate of flame region with its unique color distribution distinguished from artificial lights. If it is not a flame region then we can check to candidate of smoke region by measuring difference of brightness and chroma at present frame. If we just check flame and smoke with only simple brightness and hue, we will occasionally get false alarms. Therefore we also use motion information about candidate of flame and smoke regions. Finally, to determine the flame after motion detection, activity information is used. And in order to determine the smoke, edges detection method is adopted. As a result of simulation with real CCTV video signal, it is shown that the proposed algorithm is useful for early fire recognition.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.213-220
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• 2011

This paper proposes an effective, four-stage smoke detection method based on video that provides emergency response in the event of unexpected hazards in early fire-alarming systems. In the first phase, an approximate median method is used to segment moving regions in the present frame of video. In the second phase, a color segmentation of smoke is performed to select candidate smoke regions from these moving regions. In the third phase, a feature extraction algorithm is used to extract five feature parameters of smoke by analyzing characteristics of the candidate smoke regions such as area randomness and motion of smoke. In the fourth phase, extracted five parameters of smoke are used as an input for a K-nearest neighbor (KNN) algorithm to identify whether the candidate smoke regions are smoke or non-smoke. Experimental results indicate that the proposed four-stage smoke detection method outperforms other algorithms in terms of smoke detection, providing a low false alarm rate and high reliability in open and large spaces.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.39-55
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• 2019

In this study, we have proposed an improved method to detect forest fires by correcting the reflected signals of day images using the middle-wavelength infrared (MWIR) channel. The proposed method is allowed to remove the reflected signals only using the image itself without an existing data source such as a land-cover map or atmospheric data. It includes the processing steps for calculating a solar-reflected signal such as 1) a simple correction model of the atmospheric transmittance for the MWIR channel and 2) calculating the image-based reflectance. We tested the performance of the method using the MODIS product. When compared to the conventional MODIS fire detection algorithm (MOD14 collection 6), the total number of detected fires was improved by approximately 17%. Most of all, the detection of fires improved by approximately 30% in the high reflection areas of the images. Moreover, the false alarm caused by artificial objects was clearly reduced and a confidence level analysis of the undetected fires showed that the proposed method had much better performance. The proposed method would be applicable to most satellite sensors with MWIR and thermal infrared channels. Especially for geostationary satellites such as GOES-R, HIMAWARI-8/9 and GeoKompsat-2A, the short acquisition time would greatly improve the performance of the proposed fire detection algorithm because reflected signals in the geostationary satellite images frequently vary according to solar zenith angle.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.83-98
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• 2021

The purpose of this study to find the factors influencing the fire alarms using IoT firefighting facility management system data of Seoul Fire & Disaster Headquarters, and to present academic implications for establishing an effective prevention system of fire situation. As the number of high and complex buildings increases and former bulidings are advanced, the fire detection facilities that can quickly respond to emergency situations are also increasing. However, if the accuracy of the fire situation is incorrectly detected and the accuracy is lowered, the inconvenience of the residents increases and the reliability decreases. Therefore, it is necessary to improve accuracy of the system through efficient inspection and the internal environment investigation of buildings. The purpose of this study is to find out that false detection may occur due to building characteristics such as usage or time, and to aim of emphasizing the need for efficient system inspection and controlling the internal environment. As a result, it is found that the size(total area) of the building had the greatest effect on the fire alarms, and the fire alarms increased as private buildings, R-type receivers, and a large number of failure or shutoff days. In addition, factors that influencing fire alarms were different depending on the main usage of the building. In terms of time, it was found to follow people's daily patterns during weekdays(9 am to 6 pm), and each peaked around 10 am and 2 pm. This study was claimed that it is necessary to investigate the building environment that caused the fire alarms, along with the system internal inspection. Also, it propose additional recording of building environment data in real-time for follow-up research and system enhancement.