• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.125-132
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• 2011

Fire monitoring system detects a fire based on the values of various sensors, such as smoke, CO, temperature, or change of temperature. It detects a fire by comparing sensed values with predefined threshold values for each sensor. However, to prevent a fire it is required to predict a situation which has a possibility of fire occurrence. In this work, we propose a fire recognition system using a fuzzy inference method. The rule base is constructed as a combination of fuzzy variables derived from various sensed values. In addition, in order to solve generalization and formalization problems of rule base construction from expert knowledge, we analyze features of fire patterns. The constructed rule base results in an improvement of the recognition accuracy. A fire possibility is predicted as one of 3 levels(normal, caution, danger). The training data of each level is converted to fuzzy rules by FCM(fuzzy C-means clustering) and those rules are used in the inference engine. The performance of the proposed approach is evaluated by using forest fire data from the UCI repository.

• Fire Science and Engineering
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• v.24 no.5
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• pp.10-25
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• 2010

As disaster potential power of modern society grows larger, to improve and reinforce efficiently a national system which prepares and responds disasters, analyzed the survey for government officials of the department disaster management. Following is the contents of this research. First, cooperative relationship to disaster management organizations. Second, necessity of law establishment related crisis and disaster department. Third, by recognition regarding disaster management situational variable, overall recognition regarding disaster management situation, overall recognition regarding crisis type, recognition regarding occurrence possibility along disaster scale. Fourth, by recognition regarding structural variable of disaster management, the National Emergency Management Agency regarding disaster management, related organization, recognition difference of local government. It is a research about confusion regarding step of prevention - preparation - correspondence - restoration.

• 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.

• Journal of the Society of Disaster Information
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• v.13 no.4
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• pp.529-536
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• 2017

In this study, the perceptions were surveyed fire risk and flash flames concerning the firefighters. The results were statistically evaluated according to age, experience and rank. More than 70% of the respondents answered that there is a possibility of unexpected flame exposure in the field of fire, but there was no recognition difference according to age, experience and rank. However, if there is an emergency situation in the field of fire, the survey on the ability to cope with crises showed that there is a difference in perception depending on the age, career, and rank of respondents. From these results, it is expected that strengthening simulation training of unexpected situation will be more urgently required in the future, and measures should be taken to minimize human accidents through improvement of standard operation procedures or supplement of fire suppression education according to unexpected situation.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.202-203
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• 2023

최근 화재 탐지 분야는 불꽃 연기의 특징과 인공지능 인식(Detection) 모델을 활용하여 탐지율을 높이려는 연구가 많이 진행되어 왔다. 기존 화재 탐지 정확도를 높이기 위한 모델 연구 이외에도 불꽃·연기의 특징을 다양한 방법으로 데이터 가공한 학습 데이터셋을 활용하는 연구들이 진행되고 있다. 본 논문에서는 화재 탐지시 불꽃/연기의 오탐지율이 높은 것을 확인하고 오탐지율을 낮추기 위해 화재 상황을 인식하여 분류하는 방법과 데이터셋을 제안한다. 제안한 모델은 동영상을 학습데이터로 활용하여 화재 상황의 특징을 추출하여 분류모델에 적용하였다. 평가는 한국정보화진흥원(NIA)에서 진행하는 화재 데이터셋을 이용하여 Yolov8, Slowfast의 모델 성능을 비교 및 분석하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1376-1383
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• 2020

Although studies on fire evacuation systems are increasing, studies on the evacuation of evacuees in indoor spaces are insufficient. According to the latest research, it has been suggested that the use of high frequency might be effective for identifying the location of evacuees indoors. Accordingly, in this paper, the authors intend to develop evacuation location recognition technology and fire evacuation guidance system using high-frequency and a smartphone. The entire system was developed, including an app server, evacuees location recognition unit, an evacuation route search, an output unit, and a speaker unit based on Wi-Fi communication. The experimental results proved the possibility of the effectiveness of the system in the fire situation data. It is expected that this study could be used as an essential study of a fire evacuation guidance system using high frequency data in case of fire.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.222-230
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• 2017

The Ministry of Public Safety and Security plans to expand fire safety education infrastructure to provide customized fire safety education, spread fire safety culture and develop a tailored fire safety education system as a part of the 2016 Citizens' Safety Improvement Policy. This study has also been designed to improve safety problems in the Republic of Korea. Even though safety education has been given, citizens aren't still able to experience a close-to-real situation. In addition, their understanding and satisfaction with the curriculum are very low. Therefore, this study offers VR fire-extinguishing experience education contents as an effective alternative. With a goal of having the participants experience fire extinguishing and evacuation drill in a virtual space, this program has the following advantages: i) safe fire-extinguishing experience; ii) UI to create fun ; iii) useful in fire-extinguishing education; iv) budget saving. we configure the VR fire experience system structure and hardware by applying UX design methodology. We also develop for VR-specific motion recognition plug-in and controller that can be feeling in HMD environment.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.4
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• pp.117-122
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• 2021

In recent years, human damage and loss of money due to various disasters such as typhoons, earthquakes, forest fires, landslides, and wars are steadily occurring, and a lot of manpower and funds are required to prevent and recover them. In this paper, we designed and developed a disaster drone system based on artificial intelligence in order to monitor these various disaster situations in advance and to quickly recognize and respond to disaster occurrence. In this study, multiple disaster drones are used in areas where it is difficult for humans to monitor, and each drone performs an efficient search with an optimal path by applying a deep learning-based optimal path algorithm. In addition, in order to solve the problem of insufficient battery capacity, which is a fundamental problem of drones, the optimal route of each drone is determined using Ant Colony Optimization (ACO) technology. In order to implement the proposed system, it was applied to a forest fire situation among various disaster situations, and a forest fire map was created based on the transmitted data, and a forest fire map was visually shown to the fire fighters dispatched by a drone equipped with a beam projector. In the proposed system, multiple drones can detect a disaster situation in a short time by simultaneously performing optimal path search and object recognition. Based on this research, it can be used to build disaster drone infrastructure, search for victims (sea, mountain, jungle), self-extinguishing fire using drones, and security drones.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.282-288
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• 1997

The purpose of this paper is to predict the situation of leak in closed space using an Artificial Neural Network (ANN). The existing system can't monitor the whole He situations with on/off signals. Especially the first stage of data determines the leak spot and intensity is disregarded in gas accidents. To complement these faults, a new prototype of monitoring system is proposed. Ihe system is composed of'sensing systenL data acquisition system computer, and ANN implemented in software and is capable of identifying the leak spot and intensity in closed space. The concentration of gas is measured at the 4 different places. The network has 3 layers that are composed of 4 input Processing Element (PE),24 hidden PEs, md 4 output PEs. The ANN has optimum condition through several experiments and as a consequence the recognition rate of93.75% is achieved finally