Journal of Information Processing Systems

  • 제13권3호
  • /
  • Pages.590-598
  • /
  • 2017
  • /
  • 1976-913X(pISSN)
  • /
  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

Fire Detection Using Multi-Channel Information and Gray Level Co-occurrence Matrix Image Features

  • 투고 : 2016.06.15
  • 심사 : 2017.01.28
  • 발행 : 2017.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  1. National Fire Information Center, Ministry of Public Safety and Security, "Fire statistic status" [Online]. Available: http://www.nfds.go.kr/index.jsf.
  2. J. R. Gonzalez, M. Palahi, A. Trasobares, and T. Pukkala, "A fire probability model for forest stands in Catalonia (north-east Spain)," Annals of Forest Science, vol. 63, no. 2, pp. 169-176, 2006. https://doi.org/10.1051/forest:2005109
  3. P. Patel and S. Tiwari, "Flame detection using image processing techniques," International Journal of Computer Application, vol. 58, no. 18, pp. 13-16, 2012. https://doi.org/10.5120/9381-3817
  4. J. Seebanrungsat, S. Praising, and P. Riyamongkol, "Fire detection in the buildings using image processing," in Proceedings of 2014 3rd ICT International Student Project Conference, Nakhon Pathom, Thailand, 2014, pp. 95-98.
  5. W. Wang and H. Zhou, "Fire detection based on flame color and area," in Proceedings of 2012 IEEE International Conference on Computer Science and Automation Engineering, Zhangjiajie, China, 2012, pp. 222-226.
  6. O. John, and S. Prince, "Classification of flame and fire images using feed forward neural network," in Proceedings of 2014 International Conference on Electronics and Communication Systems, Coimbatore, India, 2014, pp. 1-6.
  7. J. S. Park, H. T. Kim, and Y. S. Yu, "Video based fire detection algorithm using Gaussian mixture model," Journal of the Korea Institute of Electronic Communication Sciences, vol. 6, no. 2, pp. 206-211, 2011.
  8. J. Kwak, D. Kim, B. Ko, and J. Nam, "Forest smoke detection using Random Forest," Proceedings of Korea Computer Congress 2011, vol. 38, no. 1, pp. 351-353, 2011.
  9. R. M. Haralick, K. Shanmugam, and I. Dinstein, "Texture features for image classification," IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610-621, 1973. https://doi.org/10.1109/TSMC.1973.4309314
  10. K. Poobalan, and S. C. Liew, "Fire detection based on color filters and Bag-of-Features classification," in Proceedings of IEEE Student Conference on Research and Development, Kuala Lumpur, Malaysia, 2015, pp. 389-392.