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Flame and Smoke Detection Method for Early and Real-Time Detection of Tunnel Fire (터널 화재의 실시간 조기 탐지를 위한 화염 및 연기 검출 기법)

Lee, Byoung-Moo;Han, Dong-Il
- 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.
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Development of Fire Detection Model for Underground Utility Facilities Using Deep Learning : Training Data Supplement and Bias Optimization (딥러닝 기반 지하공동구 화재 탐지 모델 개발 : 학습데이터 보강 및 편향 최적화)

Kim, Jeongsoo;Lee, Chan-Woo;Park, Seung-Hwa;Lee, Jong-Hyun;Hong, Chang-Hee
- Journal of the Korea Academia-Industrial cooperation Society
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- v.21 no.12
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- pp.320-330
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- 2020
Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.
https://doi.org/10.5762/KAIS.2020.21.12.320 인용 PDF KSCI

An Intelligent Fire Learning and Detection System Using Convolutional Neural Networks (컨볼루션 신경망을 이용한 지능형 화재 학습 및 탐지 시스템)

Cheoi, Kyungjoo;Jeon, Minseong
- 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.
https://doi.org/10.3745/KTSDE.2016.5.11.607 인용 PDF KSCI