• Journal of the Korean Society of Safety
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• v.28 no.6
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• pp.11-16
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• 2013

This paper presents a study on the analyzing reliability of smoke fire detector using accelerated life test. In general, the smoke fire detector is broken by dust which flow in smoke detection chamber. In order to conduct accelerated life test of smoke fire detector dust is set accelerated factor in this paper. The dust is fly-ash which is test particle 5th regulated by KS A 0090. The dust accelerated level is 60 g, 180 g and 360 g and failure time is measured by smoke sensitivity testing. It is considered to failure of detector if detector don't operate within 30 secconds when subjected to an air stream having a velocity of 20 cm/s~40 cm/s containing smoke with a concentration of 15% of rate of light-response of 1 m. The goodness of fit test and mean life prediction conduct using the failure time. The result show that life distribution fits the weibull distribution for failure time data and the mean lifes calculate 22.5 year in domestic product and 14.7 years in overseas product applied dust stress only.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.533-538
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• 2014

This study examines the performance of and searches for improvements to the existing automatic fire detection systems installed at domestic apartment buildings as a basic study for development of intelligent fire detection systems specifically for apartments. Thus, this study aims to find out the problems in performance and maintenance of the existing fire detectors installed at apartment buildings which is the prerequisite process for development of intelligent fire detection system for that specific application. It is also found impossible to check whether or not the detectors installed at each apartment are in an operational state at normal times. This study finds that it is desirable to replace the slow-sensing heat detectors by a smoke and single smoke detectors which can detect a fire at its early stage in an effort to improve the problems of fire detectors installed at apartment buildings presently. Because we need to the independence fire detection system of apartment building.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.3
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• pp.261-273
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• 2024

This paper is an experimental study on the improvement of smoke and flame detection from different materials with YOLO. For the study, images of fires occurring in various materials were collected through an open dataset, and experiments were conducted by changing the main factors affecting the performance of the fire object detection model, such as the bounding box, polygon, and data augmentation of the collected image open dataset during data preprocessing. To evaluate the model performance, we calculated the values of precision, recall, F1Score, mAP, and FPS for each condition, and compared the performance of each model based on these values. We also analyzed the changes in model performance due to the data preprocessing method to derive the conditions that have the greatest impact on improving the performance of the fire object detection model. The experimental results showed that for the fire object detection model using the YOLOv5s6.0 model, data augmentation that can change the color of the flame, such as saturation, brightness, and exposure, is most effective in improving the performance of the fire object detection model. The real-time fire object detection model developed in this study can be applied to equipment such as existing CCTV, and it is believed that it can contribute to minimizing fire damage by enabling early detection of fires occurring in various materials.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.26-31
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• 2014

In this paper, we present performance analysis of video smoke detection based on BPN-Network that is using multi-smoke feature, and Neural Network. Conventional smoke detection method consist of simple or mixed functions using color, temporal, spatial characteristics. However, most of all, they don't consider the early fire conditions. In this paper, we analysis the smoke color and motion characteristics, and revised distinguish the candidate smoke region. Smoke diffusion, transparency and shape features are used for detection stage. Then it apply the BPN-Network (Back-Propagation Neural Network). The simulation results showed 91.31% accuracy and 2.62% of false detection rate.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.77-80
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• 2023

Since fires in uncontrolled environments pose serious risks to society and individuals, many researchers have been investigating technologies for early detection of fires that occur in everyday life. Recently, with the development of deep learning vision technology, research on fire detection models using neural network backbones such as Transformer and Convolution Natural Network has been actively conducted. Vision-based fire detection systems can solve many problems with physical sensor-based fire detection systems. This paper proposes a fire detection method using the latest YOLOv8, which improves the existing fire detection method. The proposed method develops a system that detects sparks and smoke from input images by training the Yolov8 model using a universal fire detection dataset. We also demonstrate the superiority of the proposed method through experiments by comparing it with existing methods.

• Fire Science and Engineering
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• v.26 no.1
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• pp.74-79
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• 2012

This study is described for development of the stand-alone smoke and heat detector (SASHD) according to the revised in 2011 type approval and performance inspection code for detector. The main improvement of the revised regulation is source. CMOS microcontroller with nano watt technology is use for development of the workable SASHD over 10 years. The low-power SASHD is developed by using the power-saving sleep mode of microcontroller, by making the low-power source voltage checker, heat detector and smoke detector. The stand-alone detector is developed by smoke and heat detector type for reduce false fire alarm. User can choose type of work between the heat detection mode and smoke & heat detection mode. The SASHD can communicate with each them using RS-485 communication supported from microcontroller. So, this study can develop the SASHD that is able to alarm more wide area when fire occurs and reduce a flash fire alarm.

• Journal of Korean Institute of Fire Science & Engineering
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• v.4 no.1
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• pp.1-5
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• 2008

• Fire Science and Engineering
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• v.17 no.3
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• pp.13-19
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• 2003

In this paper, numerical calculations are conducted to predict the characteristics of the heat transfer and smoke propagation in a cydodrome. The gas flow velocity and temperature around the origin of the fire is obtained by using a plume model and the turbulent flow characteristics are considered by standard $textsc{k}$-$\varepsilon$ turbulent model. In this study, the transient thermal behavior can be used for designing fire detection of large rooms.