• Fire Science and Engineering
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• v.27 no.6
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• pp.77-82
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• 2013

This research suggested smart fire recognition system which trances firing location with CCD camera with wired/wire-less TCP/IP function and Pan/Tilt function, delivers information in real time to android system installed by smart mobile communication system and controls fire and disaster remotely. To embody suggested method, firstly, algorithm which applies hue saturation intensity (HSI) Transform for input video, eliminates surrounding lightness and unnecessary videos and segmentalized only firing videos was suggested. Secondly, Pan/Tilt function traces accurate location of firing for proper control of firing. Thirdly, android communication system installed by mobile function confirms firing state and controls it. To confirm the suggested method, 10 firing videos were input and experiment was conducted. As the result, all of 10 videos segmentalized firing sector and traced all of firing locations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.65-70
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• 2023

In this paper, we used a deep learning model to detect and segment flame and smoke in real time from fires. To this end, well known U-NET was used to separate and divide the flame and smoke of the fire using multi-class. As a result of learning using the proposed technique, the values of loss error and accuracy are very good at 0.0486 and 0.97996, respectively. The IOU value used in object detection is also very good at 0.849. As a result of predicting fire images that were not used for learning using the learned model, the flame and smoke of fire are well detected and segmented, and smoke color were well distinguished. Proposed method can be used to build fire prediction and detection system.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.159-164
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• 2019

Detecting wildfire using only color in image information is a very difficult issue. This paper proposes an algorithm to detect forest fire area by analyzing color and motion of the area in the video including forest fire. The proposed algorithm removes the background region using the Gaussian Mixture based background segmentation algorithm, which does not depend on the lighting conditions. In addition, the RGB channel is changed to an HSV channel to extract flame candidates based on color. The extracted flame candidates judge that it is not a flame if the area moves while labeling and tracking. If the flame candidate areas extracted in this way are in the same position for more than 2 minutes, it is regarded as flame. Experimental results using the implemented algorithm confirmed the validity.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.521-525
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• 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 Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.626-632
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• 2017

Since natural disaster occurs increasingly and becomes complicated, it causes deaths, disappearances, and damage to property. As a result, there is a growing interest in the development of ICT-based natural disaster response technology which can minimize economic and social losses. In this letter, we introduce the main functions of the forest fire management platform by using images from an UAV. In addition, we propose a disaster image analysis technology based on the deep learning which is a key element technology for disaster detection. The proposed deep learning based disaster image analysis learns repeatedly generated images from the past, then it is possible to detect the disaster situation of forest-fire similar to a person. The validity of the proposed method is verified through the experimental performance of the proposed disaster image analysis technique.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.77-79
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• 2022

A large amount of related images are required to detect images with variable shapes. Therefore, in this paper, fire images among images with variable shapes are multiplied through GAN algorithms, and detection rates when AI learning is performed using this image are compared to analyze whether the multiplied images are suitable for learning data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.747-749
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• 2003

For forest fire monitoring in relatively cool area like Siberia, design of Decision Support System (DSS) is proposed. The DSS is consisted of three different algorithms to detect potential fires from NOAA AVHRR image. The algorithm developed by CCRS (Canada Center for Remote Sensing) uses fixed thresholds for multi-channel information like one by ESA (European Space Agency). The algorithm of IGBP (International Geosphere Biosphere Program) involves contextual information in deriving fire pixels. CCRS and IGBP algorithms are rather liberal compared to more conservative ESA algorithm. Fire pixel information from the three algorithms is presented to the user. The user considers all these information in making decision about the location fire takes place.

• 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 the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.104-110
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• 2016

To get the empirical data of GPR detection and to develop the image prosessing program of GPR detection data, GPR detection were proceed by the underground pipes and cavities buried in the Chamber. In the case of non pavement and asphalt pavement, water filled cavity that was buried in 0.7m depth was able to detection. But in the case of 1.0 m and 1.3 m buring depth, water filled cavity was not able to detection. In the case of non-reinforced and reinforced concrete pavement, it was difficult to detect the cavity caused by signal interference. GPRiPP programs was developed for image processing of the GPR detection data. The major processing algorithm were background removal, stacking and gain function. With proper image processing of gain function and background removal in GPRiPP program, it was showed that similar results can be obtained with conventional image processing program.

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