• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.219-220
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• 2007

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 the tunnel, it is necessary to have a system to minimize and to discover the incident as fast as possible. The fire and smoke detection is different from the forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so that an indigenous algorithm has to be developed. The two algorithms proposed in this paper, are able to detect the exact position, at the earlier stage of incident.

• 한국산업정보학회논문지
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• 제17권7호
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• pp.89-94
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• 2012

엘리베이터 내에서는 흡연이 금지되어 있는 것이 사실이다. 엘리베이터 내에서 흡연을 하는 것은 도덕에 어긋나는 잘못된 일이다. 또한, 흡연은 우리 아이들과 여성들에게 매우 치명적일 수 있다. 본 논문에서는 엘리베이터 내에서 흡연을 하는 사람을 추출하여 포렌식 증거 자료로 법원에 제출하기 위해서이다. 엘리베이터에 탄 사람의 얼굴 주위를 부분적으로 장면 전환 검출하여 추출한다. 방법은 흰색 막대를 입에 무는 사람을 추출하는 것이다. 연기를 내뿜는 것의 추출은 향후에 진행할 것이다. 방법은 장면 전환 검출에서 컬러 히스토그램 방법으로 추출한다. 이렇게 추출하면 장면 전환 검출을 하지 않는 방법 보다 훨씬 추출률이 정확하다.

• 반도체디스플레이기술학회지
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• 제23권1호
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• pp.110-116
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• 2024

Despite the continuous advancement of science and technology, fire accidents continue to occur without decreasing over time, so there is a constant need for a system that can accurately detect fires at an early stage. However, because most existing fire detection systems detect fire in the early stage of combustion when smoke is generated, rapid fire prevention actions may be delayed. Therefore we propose an early fire detection system that can perform early fire detection at a reasonable cost using LSTM, a deep learning model based on multi-gas sensors with high selectivity in the early stage of decomposition rather than the smoke generation stage. This system combines multiple gas sensors to achieve faster detection speeds than traditional sensors. In addition, through window sliding techniques and model light-weighting, the false alarm rate is low while maintaining the same high accuracy as existing deep learning. This shows that the proposed fire early detection system is a meaningful research in the disaster and engineering fields.

• International Journal of Safety
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• 제11권1호
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• pp.6-10
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• 2012

This paper presents a study on the analysis for reducing the number of false alarms in fire detection system. In order to intelligent algorithm fuzzy logic is adopted in developing fire detection system to reduce false alarm. The intelligent fire detection algorithm compared and analyzed the fire and non-fire signatures measured in circuits simulating flame fire and smoldering fire. The algorithm has input variables obtained by fire experiment with K-type thermocouple and optical smoke sensor. Also triangular membership function is used for inference rules. And the antecedent part of inference rules consists of temperature and smoke density, and the consequent part consists of fire probability. A fire-experiment is conducted with paper, plastic, and n-heptane to simulate actual fire situation. The results show that the intelligent fire detection algorithm suggested in this study can more effectively discriminate signatures between fire and similar fire.

• 한국안전학회지
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• 제19권1호
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• pp.49-55
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• 2004

This study presents an analysis of comparison of P-type fire detection system with fuzzy logic-applied fire detection system. The fuzzy logic-applied fire detection system has input variables obtained by fire experiment of small scale with K-type temperature sensor and optical smoke sensor. And the antecedent part of fuzzy rules consists of temperature and smoke density, and the consequent part consists of fire probability. Also triangular fuzzy membership function is used for input variables and fuzzy rules. To calculate the final fire probability a centroid method is introduced. A fire experiment is conducted with controlling wood crib layer, cigarette to simulate actual fire and false alarm situation. The results show that peak fire probability is 25[%] for non-fire and is more than 80[%] for fire situation, respectively. The fuzzy logic-applied fire detection system suggested here is able to distinguish fire situation and non-fire situation very precisely.

• 한국화재소방학회논문지
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• 제20권2호
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• pp.8-13
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• 2006

제연설비와 스프링클러설비의 필요성을 확인하고 연기확산 가능성을 조사하기 위해 청정실 화재의 시뮬레이션을 수행하였다. 바닥면적 $39m{\times}13m$, 높이 4 m의 작업구역에 1MW의 메탄올 화재가 발생했을때, 제연설비와 스프링클러설비의 정상작동과 작동실패에 따른 연기누출 여부를 조사하였다. 제연설비가 정상적으로 작동하는 경우에는 스프링클러설비에 관계없이 연기가 안전하게 배출됨을 알 수 있었다. 화재확산과 연기발생을 억제하고 발생한 연기를 신속히 배출하기 위해 스프링클러설비가 필요하며, 스프링클러설비와 제연설비가 신속하게 작동할 수 있도록 신뢰도가 높은 스프링클러설비 헤드와 자동화재탐지설비가 필요함을 확인하였다.

• 해양환경안전학회지
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• 제27권1호
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• pp.22-28
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• 2021

본 연구는 화재진압 및 피난활동을 지원하는 딥러닝 기반의 알고리즘 개발에 관한 기초 연구로 선박 화재 시 연기감지기가 작동하기 전에 검출된 연기 데이터를 분석 및 활용하여 원격지까지 연기가 확산 되기 전에 연기 확산거리를 예측하는 것이 목적이다. 다음과 같은 절차에 따라 제안 알고리즘을 검토하였다. 첫 번째 단계로, 딥러닝 기반 객체 검출 알고리즘인 YOLO(You Only Look Once)모델에 화재시뮬레이션을 통하여 얻은 연기 영상을 적용하여 학습을 진행하였다. 학습된 YOLO모델의 mAP(mean Average Precision)은 98.71%로 측정되었으며, 9 FPS(Frames Per Second)의 처리 속도로 연기를 검출하였다. 두 번째 단계로 YOLO로부터 연기 형상이 추출된 경계 상자의 좌표값을 통해 연기 확산거리를 추정하였으며 이를 시계열 예측 알고리즘인 LSTM(Long Short-Term Memory)에 적용하여 학습을 진행하였다. 그 결과, 화재시뮬레이션으로부터 얻은 Fast 화재의 연기영상에서 경계 상자의 좌표값으로부터 추정한 화재발생~30초까지의 연기 확산거리 데이터를 LSTM 학습모델에 입력하여 31초~90초까지의 연기 확산거리 데이터를 예측하였다. 그리고 추정한 연기 확산거리와 예측한 연기 확산거리의 평균제곱근 오차는 2.74로 나타났다.

• IEIE Transactions on Smart Processing and Computing
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• 제6권4호
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• pp.237-245
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• 2017

This paper presents a fire detection algorithm using color and motion models from video sequences. The proposed method detects change in color and motion of overall regions for detecting fire, and thus, it can be implemented in both fixed and pan/tilt/zoom (PTZ) cameras. The proposed algorithm consists of three parts. The first part exploits color models of flames and smoke. The candidate regions in the video frames are extracted with the hue-saturation-value (HSV) color model. The second part models the motion information of flames and smoke. Optical flow in the fire candidate region is estimated, and the spatial-temporal distribution of optical flow vectors is analyzed. The final part accumulates the probability of fire in successive video frames, which reduces false-positive errors when fire-like color objects appear. Experimental results from 100 fire videos are shown, where various types of smoke and flames appear in indoor and outdoor environments. According to the experiments and the comparison, the proposed fire detection algorithm works well in various situations, and outperforms the conventional algorithms.

• Journal of Information Processing Systems
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• 제13권3호
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• pp.590-598
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• 2017

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multichannel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.1027-1028
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• 2008

In this paper, we proposed image-processing technique for automatic real-time fire and smoke detection in tunnel fire environment. To minimize false detection of fire in tunnel we used motion information of video sequence. And this makes it possible to detect exact position of event in early stage with detection, test, and verification procedures. In addition, by comparing false detection elimination results of each step, we have proved the validity and efficiency of proposed algorithm.