• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.3-4
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• 2021

폐쇄된 터널 내부에서는 사고가 일어날 경우 외부에서는 터널 내 상황을 알 수가 없어 경미한 사고라 하더라도 대형 후속 2차 사고로 이어질 가능성이 크다. 또한영상탐지로사고 상황의 오검출을 줄이기 위해서, 본 연구에서는기존의 많은 CNN 모델 중 보유한 데이터에 가장 적합한 모델을 선택하는 과정에서 가장 좋은 성능을 보인 VGG16 모델을 전이학습 시키고 fully connected layer의 일부 layer에 Dropout을 적용시켜 Overfitting을일부방지하는 CNN 모델을 생성한 뒤Yolo를 이용한 영상 내 객체인식, OpenCV를 이용한 영상 프레임 내에서 객체의ROI를 추출하고이를 CNN 모델과 비교하여오검출을 줄이면서 사고를 검출하는 시스템을 제안하였다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Proceedings of the Korea Contents Association Conference
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• 2012.05a
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• pp.291-292
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• 2012

본 연구는 여러 가지 센서를 이용하여 자동차 전용 도로터널의 차량 사고시의 음향을 인식하여 사고인식률을 높이는 화재 예고 시스템에 관한 연구이다. 현행의 CCTV나 자동화재탐재설비에서 감지하는 열센서나 영상전송자료를 파악하기에 앞서, 이차적 재해 가능성을 유의미한 수준에서 미리 예고하고 대응할 수 있는 사전예고시스템을 구성하였다. 유선설치기반의 센서로 대부분 구성된 도로터널 내에서 비교적 설비가 저렴한 무선센서를 사용함으로서 기존 터널에서의 적용성을 증대시켰다.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.219-227
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• 2012

This paper describes the design and implementation of an efficient image security system for CCTV using the analysis technique of color informations. In conventional approaches, the compression and encryption techniques are mainly used for reducing the data size of the original images while the analysis technique of color information is first proposed, which eliminates the overlapping part of the original image data in our approach. In addition, security-enhanced CCTV image security system is presented using SSL/VPN tunneling technique. When we use the method proposed in this paper, an efficient image processing is enable for a mount of information, and also security problem is enhanced. Through the implementation results, the proposed method showed that the original image information are dramatically reduced.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.499-504
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• 2006

터널 내 화재 발생 시 대규모의 인명, 재산 피해가 발생하는데 이러한 상황을 조기에 탐지함으로써 피해를 최소화하기 위한 시스템이 필요하다. 또한 터널 내 설치된 CCTV를 사람이 24시간 감시하기에는 너무 어려운 점이 많다. 이에 따라 적절한 영상 처리를 통한 화염 및 연기 검출 시스템을 통해 경보를 알려줄 경우, 보다 편리하고 사람이 모니터 앞에 없을 때 화재 발생 시 화재를 검출할 수 있어 피해를 최소화 할 수 있다. 본 논문에서는 영상처리 기법을 이용하여 터널 안에서 발생한 화재 및 연기를 고속으로 탐지하기 위한 알고리즘을 제안하였다. 터널 안에서의 화재 탐지는 차량 조명 및 터널내의 조명등과 같은 여러 가지 상황에 의해 산불 탐지 알고리즘과 다른 독자적인 알고리즘의 개발이 요구된다. 본 논문에서 제시한 두 가지 알고리즘은 기존 알고리즘보다 정확한 위치 탐지와 초기 단계에서의 탐지가 가능하도록 되었다. 또한 우리는 실험 결과를 통해 각각의 성능을 비교함으로써 제시한 알고리즘의 타당성을 보여주었다.

• 한국IT서비스학회:학술대회논문집
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• 2007.11a
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• pp.260-265
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• 2007

제2만덕터널은 부산시민의 생활에 반드시 필수적인 장소로 하루 10만대의 차량이 이용하는 곳이지만 터널의 노후화와 안전시설의 미비로 사고발생시 인명피해는 물론 경제적 손실 발생 가능성이 큰 터널 시설물이다. 제2만덕터널은 2005년 7윌부터 2006년 9월까지 교통사고 56건, 고장사고 44건, 화재 2건, 기타 11건의 사고가 발생한 곳으로 관리사무소 전담요원 7명이 3교대로 근무하고 있으며 CCTV 15대만으로 사고를 대비하고 있다. 터널의 중요성을 고려할 때 노후화된 터널이지만 유비쿼터스 기술 및 구조물 계측기술을 적용한 안전관리 시스템이 필요하다. 제2만덕터널은 1985년에 준공되어 현재와 같은 보다 체계적인 터널 안전 관리 기술이 적용되리 많은 터널로 USN기반 구조물, 노면, 화재, 공기, 조명센서를 이용한 실시간 모니터링과 돌발적인 교통사고 발생 시에 신속한 복구 처리를 위하여 유관기관과 연계된 긴급 지원 시스템의 구축의 좋은 모델이 될 것이다. 부산광역시에는 총 17개 터널이 있으며 그 중 11곳이 90년 이전에 구축된 것으로 본사업으로 노후화 터널의 유비쿼터스 기반의 안전관리 시스템이 개발된다면 부산을 중심으로 전국에 산재한 노후화 터널의 안전 관리 개선에 미치는 파급효과가 클 것이며 유비쿼터스 기술의 대표적 적용 사례가 될 것이다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1293-1299
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• 2012

In this paper, a real-time vehicle detection and tracking algorithms is proposed. The vehicle detection could be processed using GMM (Gaussian Mixture Model) algorithm and mathematical morphological processing with HD CCTV camera images. The vehicle tracking based on separated vehicle object was performed using Euclidean distance between detected object. In more detail, background could be estimated using GMM from CCTV input image signal and then object could be separated from difference image of the input image and background image. At the next stage, candidated objects were reformed by using mathematical morphological processing. Finally, vehicle object could be detected using vehicle size informations dependent on distance and vehicle type in tunnel. The vehicle tracking performed using Euclidean distance between the objects in the video frames. Through computer simulation using recoded real video signal in tunnel, it is shown that the proposed system works well.

• Ecology and Resilient Infrastructure
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• v.3 no.3
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• pp.177-181
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• 2016

In order to prevent the fragmentation of animal habitat due to road construction, the most widely applied solution is building animal passes worldwide. In Korea, animal passes were introduced in the early 2000s, and through trial and error, the national guidelines for them and their design standards were published in 2010. These were criticized by politicians because of their relative inefficiency considering their high construction cost and their lack of animal usage. This study investigated the extent to which animals used the facilities. For this study, two types of animal passes, eco-bridges and ecoducts, were considered and the test sites were chosen from the Baekdu Mountains. The animal usage data was captured using infra-red CCTV cameras. The results showed that various types of animals used eco-bridges and ecoducts. Interestingly various types of birds were captured by cameras and endangered animals were also in them. The season, weather, and their surrounded vegetation also had effects on their usages. The infrared CCTV allowed detailed captures of animals but the electricity shortage was one disadvantage. During the last decades, a number of eco-bridges were constructed throughout the country and now we need to focus on their monitoring and maintenance for their successful efficiency and application.