한국산업정보학회논문지 (Journal of Korea Society of Industrial Information Systems)

한국산업정보학회 (Korea Society of Industrial Information Systems)
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전처리와 특징 추출이 CNN기반 화재 탐지 성능에 미치는 효과

Effects of Preprocessing and Feature Extraction on CNN-based Fire Detection Performance

  • 이정환 (금오공과대학교 컴퓨터소프트웨어공학과) ;
  • 김병만 (금오공과대학교 컴퓨터소프트웨어공학과) ;
  • 신윤식 (금오공과대학교 컴퓨터소프트웨어공학과)
  • 투고 : 2018.06.25
  • 심사 : 2018.08.20
  • 발행 : 2018.08.31
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초록

최근 들어 머신 러닝 기술의 발달로 기존 영상 기반의 응용시스템에 딥러닝 기술을 적용하는 사례들이 늘고 있다. 이러한 맥락에서 화재 감지 분야에서도 CNN (Convolutional Neural Network)을 적용하는 시도들이 이루어지고 있다. 본 논문에서는 기존 전처리 방법과 특징 추출 방법이 CNN과 결합되었을 때 화재 탐지에 어떤 효과를 유발하는지를 검증하기 위해 인식 성능과 학습 시간을 평가해 보았다. VGG19 CNN 구조를 변경, 즉 컨볼루션층을 조금씩 늘리면서 실험을 진행한 결과, 일반적으로 전처리하지 않는 이미지를 사용한 경우가 성능이 훨씬 좋음을 확인할 수 있었다. 또한 성능적인 측면에서는 전처리 방법과 특징 추출 방법이 부정적인 영향을 미치지만 학습속도 측면에서는 많은 이득이 있음을 확인할 수 있었다.

Recently, the development of machine learning technology has led to the application of deep learning technology to existing image based application systems. In this context, some researches have been made to apply CNN (Convolutional Neural Network) to the field of fire detection. To verify the effects of existing preprocessing and feature extraction methods on fire detection when combined with CNN, in this paper, the recognition performance and learning time are evaluated by changing the VGG19 CNN structure while gradually increasing the convolution layer. In general, the accuracy is better when the image is not preprocessed. Also it's shown that the preprocessing method and the feature extraction method have many benefits in terms of learning speed.

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참고문헌

  1. http://index.go.kr/potal/stts/idxMain/selectPoSttsIdxMainPrint.do?idx_cd=1632&board_cd=INDX_001
  2. Kim, Y. J. and Kim, E.G., “Image Based Fire Detection Using Convolutional Neural Network,” Journal of the Korea Institute of Information and Communication Engineering, Vol. 20, No. 9, pp. 1649-1656, 2016. https://doi.org/10.6109/jkiice.2016.20.9.1649
  3. T. Celik and H. Demirel., "Fire Detection in Video Sequences Using a Generic Color Model," Fire Safety Journal, Vol. 44, No. 2, pp. 147-158, 2009. https://doi.org/10.1016/j.firesaf.2008.05.005
  4. Ko B. C., Ham, S. J. and Nam, J. Y., "Modeling and Formalization of Fuzzy Finite Automata for Detection of Irregular Fire flames," IEEE Trans. Circuits Syst. Video Technol., Vol. 21, pp. 1903-1912, 2011. https://doi.org/10.1109/TCSVT.2011.2157190
  5. P. Foggia, A. Saggese, M. Vento, "Real-time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion," IEEE Trans. Circuits Syst. Video Technol., Vol. 25, pp. 1545-1556, 2015. https://doi.org/10.1109/TCSVT.2015.2392531
  6. M. Mueller, P. Karasev, I. Kolesov, A. Tannenbaum, "Optical Flow Estimation for Flame Detection in Videos," IEEE Trans. Image Process., Vol. 22, pp. 2786-2797, 2013. https://doi.org/10.1109/TIP.2013.2258353
  7. B.U. Toreyin, Y. Dedeoglu, U. Gudukbay, A.E. Cetin, "Computer Vision Based Method for Real-Time Fire and Flame Detection," Pattern Recogn. Lett., Vol. 27, pp. 49-58, 2006. https://doi.org/10.1016/j.patrec.2005.06.015
  8. Luo R.C., Su K.L., "Autonomous Fire-Detection System Using Adaptive Sensory Fusion for Intelligent Security Robot," IEEE/ASME Trans. Mechatron., Vol. 12, pp. 274-281, 2007. https://doi.org/10.1109/TMECH.2007.897260
  9. P.V.K. Borges, E. Izquierdo, "A Probabilistic Approach for Vision-Based Fire Detection in Videos," IEEE Trans. Circuits Syst. Video Technol., Vol. 20, pp. 721-731, 2010. https://doi.org/10.1109/TCSVT.2010.2045813
  10. Khan M., Jamil A., and Baik, S.W., "Early Fire Detection Using Convolutional Neural Networks during Surveillance for Effective Disaster Management," Neurocomputing, Vol. 288, pp. 30-42, 2018. https://doi.org/10.1016/j.neucom.2017.04.083
  11. Frizzi S., Kaabi R., Bouchouicha M., Ginoux J. M., Moreau E. and Fnaiech F., "Convolutional Neural Network for Video Fire and Smoke Detection," IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 877-882, 2016.
  12. J. Sharma, O.-C. Granmo, M. Goodwin, and J. T. Fidje, "Deep Convolutional Neural Networks for Fire Detection in Images," in International Conference on Engineering Applications of Neural Networks, pp. 183-193, 2017.
  13. Piao, F. J., Ryu, J. G., Moon, K. S. and Kim, J.N., "A YCbCr Color Model For Fire Detection Based On Fire Movement," In Proceedings of 2010 Korea Multimedia Society Spring Conference, Vol. 13, No. 2, pp. 431-433, 2010.
  14. Park, K. W. Bang, J. S. and Kim, B. M., “Performance Evaluation of Car Model Recognition System Using HOG and Artificial Neural Network,” Journal of the Korea Industrial Information Systems Research, Vol. 21, No. 5, pp. 1-10, 2016. https://doi.org/10.9723/JKSIIS.2016.21.5.001
  15. http://srdas.github.io/DLBook/ConvNets.html