International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

Deep Learning and Color Histogram based Fire and Smoke Detection Research

  • Lee, Yeunghak (Department of Computer Engineering, Andong National University) ;
  • Shim, Jaechang (Department of Computer Engineering, Andong National University)
  • Received : 2019.04.27
  • Accepted : 2019.05.09
  • Published : 2019.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.

Keywords

OTNBCL_2019_v8n2_116_f0001.png 이미지

Figure 1. Flowchart of proposed algorithm.

OTNBCL_2019_v8n2_116_f0002.png 이미지

Figure 2. Faster R-CNN system flow.

OTNBCL_2019_v8n2_116_f0003.png 이미지

Figure 3. Example of labeling area for fire and smoke dataset image.

OTNBCL_2019_v8n2_116_f0004.png 이미지

Figure 4. The architecture of Faster R-CNN.

OTNBCL_2019_v8n2_116_f0005.png 이미지

Figure 6. The experimental results using the Faster R-CNN, (a) the results of true positive, (b) the results of false positive (fixed object), (c) the results of false positive (moving object).

OTNBCL_2019_v8n2_116_f0006.png 이미지

Figure 7. The example of other videos test for the proposed algorithm.

OTNBCL_2019_v8n2_116_f0007.png 이미지

Figure 5. Example of the fire and smoke frame sequence of test videos.

Table 1. The results of video test using general Faster R-CNN

OTNBCL_2019_v8n2_116_t0001.png 이미지

Table 2. The results of video test using prosed algorithm

OTNBCL_2019_v8n2_116_t0002.png 이미지

References

  1. B. C. Ko, S. J. Han, and J. Y. Nam, "Modeling and Formalization of Fuzzy Finite Automata for Detection of Irregular fire Flames," IEEE Transaction on Circuits and System Video Technology, Vol. 21, No. 12, pp. 1903-1912, December 2011. DOI: 10.1109/TCSVT.2011.2157190.
  2. J. Gubbi and S. Marimuthu, "Smoke detection in video using wavelets and support vector machines," Fire Safety Journal, Vol. 44, No. 8, pp. 1110-1115, 2009. DOI: https://doi.org/10.1016/j.firesaf.2009.08.003
  3. B. C. Ko, J. Y. Kwak, and J. Y. Nam, "Wildfire smoke detection using temporospatial features and random forest classifiers," Optical Engineering, Vol. 51, January 2012. DOI: https://doi.org/10.1117/1.OE.51.1.017208
  4. S. Frizzi, R. Kaabi, M. Bouchouicha, J. Ginoux, E. Moreau, and F. Fnaiech, "Convolutional Neural Network for Video Fire and Smoke Detection," IECON 2016, 2016. DOI: 10.1109/IECON.2016.7793196
  5. T. Chen, P. Wu, and Y. Chiou, "An Early Fire-Detection Method Based on Image processing," 2004 International Conference on Image Processing, pp. 1707-1707, October 2004. DOI: 10.1109/ICIP.2004.1421401
  6. B. U. Toreyin and Y. Dedeoglu, and A. E. Cetin, "Wavelet Based Real-time Smoke Detection in Video," EUSIPO 2005, 2005.
  7. C. Yu, Z. Mei, and X. Zhang, "A Real-time Video Fire Flame and Smoke Detection Algorithm," Procedia Engineering, Vol. 62, pp. 891-898, 2013. DOI: https://doi.org/10.1016/j.proeng.2013.08.140
  8. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based Learning Applied to Document Recognition," Proceeding of IEEE, Vol. 86, No. 11, pp. 2278-2324, November 1998. DOI: 10.1109/5.726791
  9. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep convolutional neural Network: Advanced in Neural Information Processing System 25," 26th Annual Conference on Neural Information Processing Systems, pp. 1106-1114, December 2012. DOI: 10.1145/3065386
  10. G. E. Hinton, N. Srivastava, A. Krizhevsky, S. Ilya, and R. Salakhutdinov, "Improving neural networks by preventing co-adaptation of feature detectors," Clinical Orthopedics and Related Research 2012, pp.1-18, July 2012. DOI: 10.1.1.258.6363
  11. Y. Lee, I. Ansari, and J. Shim, "Rear-Approaching Vehicle Detection using Frame Similarity base on Faster R-CNN," International Journal of Engineering and Technology, Vol. 7, pp. 177-180, 2018. DOI: 10.14419/ijet.v7i4.44.26979
  12. Wikipedia, Color histogram, Web URL: https://en.wikipedia.org/wiki/Color_histogram/
  13. Receiver operating characteristic, Web URL:https://en.wikipedia.org/wiki/Receiver_operating_characteristic/
  14. Y. Zhang, X. Wang, and B. Qu, "Three-Frame Difference Algorithm Research Based on Mathematical Morphology," Procedia Engineering, Vol. 29, pp. 2705-2709, December 2012. DOI: 10.1016/j.proeng.2012.01.376