DOI QR코드

DOI QR Code

Implementation of Image based Fire Detection System Using Convolution Neural Network

합성곱 신경망을 이용한 이미지 기반 화재 감지 시스템의 구현

  • 방상완 (송원대학교 컴퓨터정보학과)
  • Received : 2017.02.13
  • Accepted : 2017.04.24
  • Published : 2017.04.30

Abstract

The need for early fire detection technology is increasing in order to prevent fire disasters. Sensor device detection for heat, smoke and fire is widely used to detect flame and smoke, but this system is limited by the factors of the sensor environment. To solve these problems, many image-based fire detection systems are being developed. In this paper, we implemented a system to detect fire and smoke from camera input images using a convolution neural network. Through the implemented system using the convolution neural network, a feature map is generated for the smoke image and the fire image, and learning for classifying the smoke and fire is performed on the generated feature map. Experimental results on various images show excellent effects for classifying smoke and fire.

화재 재해를 예방하기 위해 조기 화재 탐지 기술의 필요성이 증대되고 있다. 화염 및 연기를 감지하기 위해 열, 연기 및 불꽃에 대한 센서 감지 장치가 널리 사용되고 있으나, 이 시스템은 센서 주변 환경의 요소에 따라 제한된다. 이 문제들을 해결하기 위해 다수의 이미지 기반 화재 탐지 시스템이 개발되고 있다. 본 논문에서는 카메라 입력 이미지로 부터 합성곱 신경망을 이용하여 연기 이미지와 불꽃 이미지에 대한 학습을 통해 특징 맵을 추출하고, 이를 사용하여 다른 입력 이미지를 연기와 불꽃으로 분류하는 이미지 기반 화재 감지 시스템을 구현하였다. 다양한 조건의 이미지를 대상으로 실험한 결과 연기와 불꽃으로 분류하는데 우수한 성능을 보여주었다.

References

  1. C. Liu and N. Ahuja, "Vision Based Fire Detection," Proc. of the 17th Int. Conf. on Pattern Recognition, Cambridge, UK, Aug. 2004, pp. 134-137.
  2. D. Zhang, S. Han, J. Zhao, and Z. Zhang, "Image based forest fire detection using dynamic characteristics with artificial neural networks," Int. Joint Conf. on Artificial Intelligence, Hainan Island, China, Apr. 2009, pp. 290-293.
  3. T. Celik, H. Ozkaramanlt, and H. Demirel, "Fire pixel classification using fuzzy logic and statistical color model," IEEE Trans. Acoustics, Speech and Signal Processing, vol. 1, no. 2, 2007, pp. 1205-1208.
  4. T. Hhen and P. Hu, "An Early Fire Detection Method Based on Image Processing," Int. Conf. on Image Processing, Singapore, Oct. 2004, pp. 1707-1710.
  5. B. Toreyina and Y. Dedeoglub, "Computer vision based method for real-time fire and flame detection," Pattern Recognition Letters, vol. 27, issue 1, 2006, pp. 49-58. https://doi.org/10.1016/j.patrec.2005.06.015
  6. J. Park, H. Kim, and Y. Yu, "Video Based Fire Detection Algorithm using Gaussian Mixture Model," J. of the Korea Institute of Electronic Communication Sciences, vol. 6, no. 2, 2011, pp. 206-211.
  7. H. Kim and J. Park, "Fire Detection in Outdoor Using Statistical Characteristics of Smoke," J. of the Korea Institute of Electronic Communication Sciences, vol. 9, no. 2, 2014, pp. 149-154. https://doi.org/10.13067/JKIECS.2014.9.2.149
  8. S. Mang and J. Kim, "Survey for Early Detection Techniques of Smoke and Flame using Camera Images," J. of the Korea Society of Computer and Information, vol. 16, issue 4, 2011, pp. 43-52. https://doi.org/10.9708/jksci.2011.16.4.043
  9. K. Cheoi, "An Intelligent Fire Learning and Detection System Using Convolutional Neural Networks," Korea Information Processing Society Trans. Software and Data Engineering, vol. 5, no. 11, Nov. 2016, pp. 607-614.
  10. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. of the IEEE, vol. 86, issue 11, Nov. 1998, pp. 2278-2324. https://doi.org/10.1109/5.726791
  11. E. Lee and W. Lee, "Recognition of characters on car number plate and best recognition ratio among their layers using Multi-layer Perceptron," J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 1, 2016, pp. 73-80. https://doi.org/10.13067/JKIECS.2016.11.1.73
  12. A. L. Maas, A. Y. Hannun, A. Y. Ng, "Rectifier Nonlinearities Improve Neural Network Acoustic Models," Proc. of the 30th Int. Conf. on Machine Learning, Atlanta, USA, June 2013.