• Fire Science and Engineering
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• v.34 no.2
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• pp.30-40
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• 2020

In the event of a fire and a disaster, prompt and accurate alarms inside and outside the building are directly related to the minimization of damage and the success of life evacuation. However, due to unwanted fire alarms in automated fire detection systems, the number of dispatches by misunderstanding in the 119 service is increasing. This causes the insensitivity to the safety of building managers and the waste of the fire-fighting power. Therefore, in this study, the statistical databases and literature on unwanted fire alarms in Korea and abroad (USA, UK) were identified and the management systems for unwanted fire alarms were compared and analyzed to identify problems of statistics in the management systems for unwanted fire alarms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1506-1514
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• 2015

In this paper, we proposed a forest fires prediction and detection system. It could provide a situation of fire prediction and detection methods using context awareness sensor. A fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire in complex situations. In addition, it is possible to differential management of intensive fire detection and prediction for required dividing the state of fire zone. Therefore we propose an algorithm to determine the prediction and detection from the fire parameters as an temperature, humidity, Co2 and the flame in real-time by using a context awareness sensor and also suggest algorithm that provide the path of fire diffusion and service the secure safety zone prediction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1596-1603
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• 2018

Rapidly detecting and warning of fires is necessary for minimizing human injury and property damage. Generally, when fires occur, both the smoke and the flames are generated, so fire detection systems need to detect both the smoke and the flames. However, most fire detection systems only detect flames or smoke and have the disadvantage of slower processing speed due to additional preprocessing task. In this paper, we implemented a fire detection system which predicts the flames and the smoke at the same time by constructing a CNN model that supports multi-labeled classification. Also, the system can monitor the fire status in real time by using Grad-CAM which visualizes the position of classes based on the characteristics of CNN. Also, we tested our proposed system with 13 fire videos and got an average accuracy of 98.73% and 95.77% respectively for the flames and the smoke.

• Fire Science and Engineering
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• v.28 no.6
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• pp.69-75
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• 2014

Recently Amore pacific rack-type warehouse fire broke out and argue an urgent improvement of fire protection design code including automatic sprinkler and detection design. Various type of commodities have their unique fire characteristics from fire spread rate and heat lease rate and fire hazard depends on storage height, rack arrangement, aisle width, fire load etc. With increasing ceiling height for more storage space prevent effective water spray of sprinkler head, also delays detection time causes failure of early suppression. To achieve fire protection code performance of this occupancy, Major code articles relating to a classification of commodity, sprinkler system installation, detection and fire fighting are reviewed and suggested based on fire case analysis, code review between country and field survey.

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

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.3
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• pp.155-161
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• 2013

To reduce the personnel and material loss caused by fire, we propose the automatic fire extinguishing system based on the ignition point tracking using the flame detecter. This automatic fire extinguishing system is composed of the flame detecting system and the fire extinguishing system based on the water cannon. We study the method for the ignition point tracking and the automatic fire extinguishing using the water cannon and the flame detecter. The flame detecting system for the early fire detection and the ignition point tracking has to be satisfied the requirement of the detecting range and the flame detection time. So we study the signal process algorithm for an improvement of the flame detecting system.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2002.11a
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• pp.178-183
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• 2002

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2001.11a
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• pp.264-270
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• 2001

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1149-1151
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• 2003

We performed a validation study of the MODIS active fire detection algorithm using high resolution SPOT image as the reference data set. Fire with visible smoke plumes are detected in the SPOT scenes, while the hotspots in MODIS data are detected using NASA's new version 4 fire detection algorithm. The detection performance is characterized by the commission error rate (false alarms) and the omission error rate (undetected fires). In the Sumatra and Kalimantan study area, the commission rate and the omission rate are 27% and 34% respectively. False alarms are probably due to recently burnt areas with warm surfaces. False negative detection occur where there are long smoke plumes and where fires occur in densely vegetated areas.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.260-267
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• 2021

In this paper, we intend to use a new mixed model of YoloV5 and DeepSort. For fire detection, we want to increase the accuracy by automatically extracting the characteristics of the flame in the image from the training data and using it. In addition, the high false alarm rate, which is a problem of fire detection, is to be solved by using this new mixed model. To confirm the results of this paper, we tested indoors and outdoors, respectively. Looking at the indoor test results, the accuracy of YoloV5 was 75% at 253Frame and 77% at 527Frame, and the YoloV5+DeepSort model showed the same accuracy at 75% at 253 frames and 77% at 527 frames. However, it was confirmed that the smoke and fire detection errors that appeared in YoloV5 disappeared. In addition, as a result of outdoor testing, the YoloV5 model had an accuracy of 75% in detecting fire, but an error in detecting a human face as smoke appeared. However, as a result of applying the YoloV5+DeepSort model, it appeared the same as YoloV5 with an accuracy of 75%, but it was confirmed that the false positive phenomenon disappeared.