터널 화재의 실시간 조기 탐지를 위한 화염 및 연기 검출 기법

Flame and Smoke Detection Method for Early and Real-Time Detection of Tunnel Fire

  • 발행 : 2008.07.25

초록

본 논문에서는 터널 환경 내에서 발생할 수 있는 화재를 조기에 실시간으로 탐지하기 위한 시각 처리 기법을 제시한다. 다양한 환경 하에서 화재 발생 시 이를 조기에 발견하여 인명 및 재산 피해를 최대한 줄이기 위한 목적을 가지고 많은 연구들이 제안되었다. 그러나 터널 화재 탐지의 경우 터널 환경이라는 특이성 때문에 기존의 화재 탐지 기법을 적용하기 어려우며, 터널 공간에 특성화된 새로운 알고리즘이 필요하다. 이에 본 논문에서는 컬러정보를 기반으로 한 화염 후보 영역 검출기법, 움직임 정보를 기반으로 한 연기 후보 영역 검출 기법을 사용하고 모폴로지 기법, 재검증 및 제거 기법을 이용하여 화재 검출 시 발생할 수 있는 오검출 영역을 제거하는 방법을 통해서 정확한 위치 탐지와 조기 탐지가 가능한 알고리즘을 개발하였다. 또한 실험 결과를 통해 각각의 성능을 비교함으로써 제시한 알고리즘의 타당성을 보여주었다.

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.

키워드

참고문헌

  1. Thou-Ho. Chen, Cheng-Liang. Kao and Sju-Mo. Chang, "An intelligent real-time fire-detection method based on video processing." In Security Technology, 2003. Proceedings. IEEE 37th Annual 2003 International Carnahan Conference on 14-16 Oct. pp.104 - 111, 2003
  2. Cappellini. V, Mattii. L. and Mecocci. A, "An intelligent system for automatic fire detection in forests" In Image Processing and its Applications Third International Conference 1989, pp. 563 - 570, 1989
  3. Noda. S and Ueda. K, "Fire detection in tunnels using an image processing method" In Vehicle Navigation and Information Systems Conference Proceedings 1994, pp. 57 - 62, 1994
  4. Cigada. A, Ruggieri. D and Zappa. E, "Road and railway tunnel fire hazard: a new measurement method for risk assessment and improvement of transit safety" In Measurement Systems for Homeland Security, Contraband Detection and Personal Safety Workshop(IMS 2005) Proceedings of the 2005 IEEE International Workshop on 29-30 March pp. 89 - 94, 2005
  5. Koga. K, Inobe. T., Namai. T and Kaneko. Y, "Integrated traffic flow monitoring system in a large-scale tunnel" In Intelligent Transportation System ITSC 97. IEEE Conference 1997, pp. 165 - 170, 1997
  6. T. Ono, H. Ishii., K. kawamura, H. Miura, E. Momma, T. Fujisawa and J. Hozumi, "Applic ati -on of neural network to analyses of CCD colour TV-camera image for the detection of car fires in expressway tunnels" In Fire safety journal, pp. 285-289, 2006
  7. Turgay. Celik, Hasan. Demirel, Huseyin. Ozkar -amanli and Mustafa. Uyguroglu, "Fire detection using statistical color model in video sequences" In Journal of Visual Communication and Image Representation, Volume 18, Issue 2, ISSN:1047-3203, pp. 176-185, 2007 https://doi.org/10.1016/j.jvcir.2006.12.003
  8. Thou-Ho(Chao-Ho). Chen, Yen-Hui. Yin, Shi-Feng. Huang. and Yan-Ting. Ye, "The smok -e detection for early fire-alarming system base on video processing" In International Conference on Intelligent Information Hiding and Multimedia, pp. 427-430, 2006
  9. Marbach G, Loepfe. M and Brupbacher. T, "An Image processing technique for fire detection in video images" In Fire safety Journal 2006; volume 9, Number 28 ISSN 1556-8849, 41(4) pp. 285-289, 10 July 2006
  10. Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing", Prentice Hall, pp.665-720, 2003