Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
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Effects of Preprocessing and Feature Extraction on CNN-based Fire Detection Performance

전처리와 특징 추출이 CNN기반 화재 탐지 성능에 미치는 효과

  • 이정환 (금오공과대학교 컴퓨터소프트웨어공학과) ;
  • 김병만 (금오공과대학교 컴퓨터소프트웨어공학과) ;
  • 신윤식 (금오공과대학교 컴퓨터소프트웨어공학과)
  • Received : 2018.06.25
  • Accepted : 2018.08.20
  • Published : 2018.08.31
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Abstract

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.

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.

Keywords

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