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

• Journal of Environmental Health Sciences
• /
• v.9 no.1
• /
• pp.85-88
• /
• 1983

캄덴 카운티 청사에 있는 transformer에 화재가 난 후 건물내에 있는 60명의 혈야에서 PCB를 측정하였다. 화재가 발생한 건물내의 공기에서는 거의 검출되지 않았으나 transformer가 있는 방의 Swab Sample에서는 $52.6 \mug/cm^2$가 검출되었다. 건물을 재사용하기 전에 환기를 최대로 시킨후 다시 Swab Sample을 취했을때 12 Sample중 10주에서 PCB가 검출되지 않았다. 또한 혈야에서 PCB수준은 미검출에서 16ppb였으나 일반인에서도 69.2ppb까지 검출된것과 그밖에 17.6ppb까지 검출된 것으로 보아 화재후 PCB가 혈야중에서 증가했다는 증거는 없다고 본다. 이와 같이 혈청내에서 PCB가 증가하지 않은것은 화재후 즉시 청소와 환기를 실시한 결과로 본다.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 1997.10a
• /
• pp.55-59
• /
• 1997

전기로 인한 재해의 가장 큰 비중을 차지하는 것이 전기 화재이다. 전기화재는 점점 증가하고 있는 추세이므로 이러한 전기 화재의 징후 검출이 가능하다면 늘어만가는 전기재해의 피해를 줄일 수 있게 된다. 이러한 전기화재는 전기 설비의 누전과 합선 및 과부하로 발생하며 이러한 현상으로 스파크를 수반하게 된다. 따라서 이러한 스파크 신호의 특성을 분석하여 전기설비의 이상현상 검출을 통하여 전기화재의 징후를 검출할 수 있게 되는 것이다. 본 논문에서는 FFT(Fast Fourier Transformation)와 DWT(Digital Wavelet Transformation) 을 이용하여 전기화재 요인으로서의 스파크 신호 특성을 분석 방법을 제시하였다.

• Journal of Advanced Navigation Technology
• /
• v.15 no.3
• /
• pp.471-477
• /
• 2011

A wide distribution of CCTV cameras in many public areas can be used not only for video surveillance systems but also for preserving 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. If spatial and temporal variances changes rapidly and close to fire motion, fire candidate is considered as fire.

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.594-597
• /
• 2010

This paper proposes a fire detection algorithm using low-cost camera to detect visual features of fire. In the previous work sensor cameras were used, but here we use very simple cameras. This method uses YCbCr and YIQ color model to detect candidate regions of fire. The candidate areas are extracted from the boundaries of the fire. noise removal elimination is performed. Regardless of environmental changes around the fire area, the results of the proposed algorithm are very satisfactory.

• Journal of the Korea Computer Graphics Society
• /
• v.28 no.4
• /
• pp.23-30
• /
• 2022

In this paper, we propose an algorithm that can accurately segment a fire even when it is surrounded by smoke of a similar color. Existing fire area segmentation algorithms have a problem in that they cannot separate fire and smoke from fire images. In this paper, the fire was successfully separated from the smoke by applying the color compensation method and the fog removal method as a preprocessing process before applying the fire area segmentation algorithm. In fact, it was confirmed that it segments fire more effectively than the existing methods in the image of the fire scene covered with smoke. In addition, we propose a method that can use the proposed fire segmentation algorithm for efficient fire detection in factories and homes.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.4
• /
• pp.59-70
• /
• 2008

In this paper, we proposed image processing technique for automatic real-time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in variety environments, it is purposeful to propose many studies to minimize and to discover the incident as fast as possible. But we need new specific algorithm because tunnel environment is quite different and it is difficult to apply previous fire detection algorithm to tunnel environment. Therefore, in this paper, we proposed specific algorithm which can be applied in tunnel environment. To minimize false detection in tunnel we used color and motion information. And it is possible to detect exact position in early stage with detection, test, verification procedures. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm.

• Proceedings of the Korea Contents Association Conference
• /
• 2009.05a
• /
• pp.521-525
• /
• 2009

In this paper, we introduce fire detection system and software learning algorithm that recognize fire patterns. Flame patterns means that periodical and consistent pattern about general conception of fire, and to process it with the definition. Learning algorithm for flame pattern recognition that we propose is the method which is faster and more exactly than existing algorithm. Also, we trying to elicit the method through experiment result and by applying it, we show the validity of an early fire warning system.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.6 no.2
• /
• pp.206-211
• /
• 2011

In this paper, a fire detection algorithm based on video processing is proposed. At the first stage, background image extracted from CCTV video input signal, and then foreground image were separated by differencing CCTV input signal from background image. At the second stage, candidated area were extracted by using color information from foreground image. At the final stage, smoke or flame characteristic area were separated by using Gaussian mixture modeling applied to candidated area, and then fire can be detected. Through real experiments at the inner room, it is shown that the proposed system works well.