• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7964-7970
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• 2015

In enclosed areas such as railway tunnels, the heat and smoke generated by a fire can pose a tremendous risk to the life of passengers. To prevent or mitigate such scenarios, fire detectors are installed for early fire detection. This numerical study is preformed for establishing the method of detecting performance of fire detectors installed on railway tunnels. Numerical analysis are conducted using the fire dynamics simulator, developed by the NIST. The temperature of the tunnel walls is determined using the assumed exterior structure of the tunnel. In addition, the detection times of detectors installed at different locations in the tunnel are obtained for different sizes of the fire source, and the results are compared and analyzed.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.425-428
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• 2012

본 논문에서는 터널 내 유고 상황을 실시간으로 빠르게 감지하여 터널 관리자에게 상황을 전달하여 터널의 안전한 운영에 도움을 줄 수 있는 컴퓨터 비전을 이용한 터널 유고감지 시스템을 제안하였다. 제안한 시스템은 관리자, 서버, 영상 검지기로 구성되며 영상 검지기의 경우 객체를 추출하기 위하여 배경차이법을 사용하였으며, 터널 내에서 발생하는 조명의 변화, 입 출입구의 조명의 영향, 카메라의 프리컬링 잡음의 영향을 최소화하였으며, 터널 내에서 발생할 수 있는 정지물체, 차량 외 통행, 연기, 역주행, 정체 지체의 유고 상황을 감지하는 방법을 개발하였다. 제안한 시스템을 전남 여수의 마래터널 및 엑스포터널, 전북 임실의 운암터널에서 실험한 결과 터널 내에서 발생하는 유고 상황을 감지하였다.

• Journal of the Korean Society of Hazard Mitigation
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• v.5 no.3 s.18
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• pp.41-46
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• 2005

Establishing special fire flower sensor of automatic fire equipment in tunnel, it is informed perception, croaker suppression realization and danger occurrence item vehicles driver of tunnel entry verge and connection stream tube institution early stage. which must do to cope immediately. In this paper, quick disposal decides to examine about dictionary perception system that is possible method.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2010.10a
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• pp.155-158
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• 2010

본 연구에서는 최근 도심지 대심도 지하도로 및 침매터널등에서 중요성이 부각되고 있는 터널내 화재안전 설계를 위한 영상유고감지시스템의 성능평가를 수행하였다. 영상유고감지시스템(VIDS)의 성능 평가를 위하여 터널 내부에서 발생할 수 있는 유고상황을 5가지로 구분하여 보행자, 낙하물, 정지차량, 역주행, 연기발생등의 상황을 인위적으로 발생시켰으며 이에 따른 감지 능력을 평가하였다. 실험결과 2, 3회 걸친 지속적인 교정과 세부조정을 거친 후에는 보행자 98.3%, 낙하물 96.7%, 정지차량 100%, 역주행 100%, 연기감지 100%의 감지율을 나타내었으며 카메라의 설치거리 100m 이내에서 비교적 높은 감지율을 나타내었다. 영상유고감지기의 적용 신뢰도는 터널내 조도, 카메라의 설치 위치에 따른 영상 변화등에 의존적이었으나 대심도 터널등의 신속한 화재감지를 위한 대안으로 적용될 수 있을 것으로 판단되었다.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.2
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• pp.83-90
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• 2015

Almost of current Automatic Incident Detection(AID) algorithms involve the vulnerability that detects the traffic accident in open road or in tunnel as the traffic jam not as the traffic accident. This paper proposes the improved accident detection algorithm to enhance the detection probability based on accident detection algorithms applied in open roads. The improved accident detection algorithm provides the preliminary judgment of potential accident by detecting the stopped object by Gaussian Mixture Model. Afterwards, it measures the detection area is divided into blocks so that the occupancy rate can be determined for each block. All experimental results of applying the new algorithm on a real incident was detected image without error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1082-1087
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• 2019

Video Incident Detection System is a detection system for the purpose of detection of an emergency in an unexpected situation such as a pedestrian in a tunnel, a falling object, a stationary vehicle, a reverse run, and a fire(smoke and flame). In recent years, the importance of the city center has been emphasized by the construction of underpasses in great depth underground space. Therefore, in order to apply Video Incident Detection System to a Double Deck Tunnel, it was developed to reflect the design characteristics of the Double Deck Tunnel. and In this paper especially, the fire detection technology, which is not it is difficult to apply to the Double Deck Tunnel environment because it is not supported on existing Video Incident Detection System or has a fail detect, we propose fire detection using color image analysis, silhouette spread, and statistical properties, It is verified through a real fire test in a double deck tunnel test bed environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1325-1334
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• 2016

The video incident detection System is a probe for rapid detecting the walker, falling, stopped, backwards, smoke situation in tunnel. Recently, the importance is increases from the downtown double deck tunnel in great depth underground space[1], but the legal basis is weak and the vulnerable situation experimental data. So, In this paper, we introduce a long-term log data analysis information in the tunnenl video incident detection system installed and experimental results in order to verify the feasibility of apply to video incident detection system for the double deck tunnel. It is proposed a few things about derives the problem of existing video incident detection system, improvements and reflect attributes for double deck tunnel. The contents described in this paper will contribute to refine the prototype of video incident detection system will apply to future double deck multi-layer tunnels.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2010.04a
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• pp.96-101
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• 2010

본 연구에서는 터널 내부에서의 화재 사고 시 화재를 자동으로 조기 발견 경보하여 화재 초기에 대응력을 확보시킬 수 있게 하는 화재 감지기의 성능 평가를 위해 터널 내부에서의 실물 화재 실험을 실시하였다. 실물화재실험은 일반적으로 도로 터널에 적용 중인 공기관식, 반도체식 감지기를 대상으로 "도로터널 방재시설 설치지침(2004, 건설교통부)"의 터널 내부의 화재 감지기 성능 실험 기준에 따라 메탄올을 이용한 풀버너와 소형 승용차를 이용하였다. 실험결과 메탄을 풀화재(단면적 $4m^2$)의 경우 약 36초 안에 모두 화재를 감지하였으며, 실물자동차화재실험의 경우는 1분 이내에 모두 화재를 감지하는 결과를 나타내었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.9-18
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• 2010

Our country has so large mountain area that the tunnel construction is inevitable and the need of incident detection that provides safe management of tunnels is increasing. In this paper, we suggest a tunnel incident detection system using computer vision techniques, which can detect the incidents in a tunnel and provides the information to the tunnel administrative office in order to help safe tunnel operation. The suggested system enhances the processing speed by using simple processing algorithm such as image subtraction, and ensures the accuracy of the system by focused on the incident detection itself rather than its classification. The system is also cost effective because the video data from 4 cameras can be simultaneously analyzed in a single PC-based system. Our system can be easily extended because the PC-based analyzer can be increased according to the number of cameras in a tunnel. Also our web-based structure is useful to connect the other remotely located tunnel incident systems to obtain interoperability between tunnels. Through the experiments the system has successfully detected the incidents in real time including dropped luggage, stoped car, traffic congestion, man walker or bicycle, smoke or fire, reverse driving, etc.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.