• Fire Science and Engineering
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• v.19 no.4 s.60
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• pp.64-68
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• 2005

In this paper, we studied fire position recognition and alarm system when we acquired CCDcamera image of fire region and position. We proposed effectively extraction system of boundary of fire region using RED Filtering, and applied 2-graylevel image method to fire boundary extraction. Finally we can make system of fire position and region using mask extraction and matching for fire recognition. For the purpose of experiment result, we effectively recognized that the tire occurrence position and region have steadily spread.

• Land and Housing Review
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• v.11 no.4
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• pp.99-104
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• 2020

This paper presents the recent research developments identified through a review of literature on the application of artificial intelligence in developing automated designs of fire protection facilities. The literature review covered research related to image recognition and applicable neural networks. Firstly, it was found that convolutional neural network (CNN) may be applied to the development of automating the design of fire protection facilities. It requires a high level of object detection accuracy necessitating the classification of each object making up the image. Secondly, to ensure accurate object detection and building information, the data need to be pulled from architectural drawings. Thirdly, by applying image recognition and classification, this can be done by extracting wall and surface information using dimension lines and pixels. All combined, the current review of literature strongly indicates that it is possible to develop automated designs for fire protection utilizing artificial intelligence.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.143-148
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• 2022

This paper attempted to analyze the correlation between the risk image of the evacuees in the tunnel and the variables that affect the evacuation behavior due to the closed feeling. As to whether there is a difference in the level of recognizing the tunnel risk image according to the distribution of jobs, the null hypothesis was rejected at the significance probability of 0.002, so it can be said that the level of recognition of the tunnel risk image varies depending on the job group. In the distribution difference between gender and tunnel risk image recognition level, the significance probability was 0.012, indicating that the null hypothesis was rejected, indicating that the tunnel risk recognition distribution according to gender was different. As a result of analyzing the distribution difference between the tunnel's closed feeling and the tunnel risk perception level, the significance probability was 0.001, and the null hypothesis was rejected, indicating that there was a difference in the tunnel risk image level.

• 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.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.536-546
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• 2022

A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.

• 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.

• Journal of IKEEE
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• v.23 no.3
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• pp.777-784
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• 2019

A pre-fire awareness and automatic notification system are required because it is possible to minimize the damage if the fire situation is precisely detected after a fire occurs in a place where people are unusual or in a mountainous area. In this study, we developed a RaspberryPi-based fire recognition system using Faster-recurrent convolutional neural network (F-RCNN) and single shot multibox detector (SSD) and demonstrated a fire alarm system that works with power line communication. Image recognition was performed with a pie camera of RaspberryPi, and the detected fire image was transmitted to a monitoring PC through an inductive power line communication network. The frame rate per second (fps) for each learning model was 0.05 fps for Faster-RCNN and 1.4 fps for SSD. SSD was 28 times faster than F-RCNN.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.505-507
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• 2021

Image recognition technology is a technology that recognizes an image object by using the generated feature descriptor and generates object feature points and feature descriptors that can compensate for the shape of the object to be recognized based on artificial intelligence technology, environmental changes around the object, and the deterioration of recognition ability by object rotation. The purpose of the present invention is to implement a power management framework required to increase profits and minimize damage to livestock farmers by preventing accidents that may occur due to the improvement of efficiency of the use of livestock house power and overloading of electricity by integrating and managing a power fire management device installed for analyzing a complex environment of power consumption and fire occurrence in a smart safety livestock house, and to develop and disseminate a safe and optimized intelligent smart safety livestock house.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.968-975
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• 2023

Purpose: We would like to confirm that the false positive rate of flames/smoke is high when detecting fires. Propose a method and dataset to recognize and classify fire situations to reduce the false detection rate. Method: Using the video as learning data, the characteristics of the fire situation were extracted and applied to the classification model. For evaluation, the model performance of Yolov8 and Slowfast were compared and analyzed using the fire dataset conducted by the National Information Society Agency (NIA). Result: YOLO's detection performance varies sensitively depending on the influence of the background, and it was unable to properly detect fires even when the fire scale was too large or too small. Since SlowFast learns the time axis of the video, we confirmed that detects fire excellently even in situations where the shape of an atypical object cannot be clearly inferred because the surrounding area is blurry or bright. Conclusion: It was confirmed that the fire detection rate was more appropriate when using a video-based artificial intelligence detection model rather than using image data.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.607-614
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• 2016

In this paper, we propose an intelligent fire learning and detection system using convolutional neural networks (CNN). Through the convolutional layer of the CNN, various features of flame and smoke images are automatically extracted, and these extracted features are learned to classify them into flame or smoke or no fire. In order to detect fire in the image, candidate fire regions are first extracted from the image and extracted candidate regions are passed through CNN. Experimental results on various image shows that our system has better performances over previous work.