Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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Development of Fire Detection Model for Underground Utility Facilities Using Deep Learning : Training Data Supplement and Bias Optimization

딥러닝 기반 지하공동구 화재 탐지 모델 개발 : 학습데이터 보강 및 편향 최적화

  • Kim, Jeongsoo (Korea BIM Research Center, Korea Institute of Civil Engineering and Building Technologies) ;
  • Lee, Chan-Woo (SPIN A WEB) ;
  • Park, Seung-Hwa (Korea BIM Research Center, Korea Institute of Civil Engineering and Building Technologies) ;
  • Lee, Jong-Hyun (SPIN A WEB) ;
  • Hong, Chang-Hee (Korea BIM Research Center, Korea Institute of Civil Engineering and Building Technologies)
  • 김정수 (한국건설기술연구원 국가BIM연구센터) ;
  • 이찬우 (스핀어웹) ;
  • 박승화 (한국건설기술연구원 국가BIM연구센터) ;
  • 이종현 (스핀어웹) ;
  • 홍창희 (한국건설기술연구원 국가BIM연구센터)
  • Received : 2020.11.02
  • Accepted : 2020.12.04
  • Published : 2020.12.31
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Abstract

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

화재는 높은 비정형성으로 인해 딥러닝 모델을 이용한 영상인식 분야에서도 좋은 성능을 내기가 어려운 대상 중 하나이다. 특히 지하공동구 내 화재는 딥러닝 모델의 학습을 위한 화재 데이터 확보가 어렵고 열약한 영상 조건 및 화재로 오인할 수 있는 객체가 많아 화재 검출이 어렵고 성능이 낮다. 이러한 이유로 본 연구는 딥러닝 기반의 지하공동구 내 화재 탐지 모델을 제안하고, 제안된 모델의 성능을 평가하였다. 기존 합성곱 인공신경망에 GoogleNet의 Inception block과 ResNet의 skip connection을 조합하여 어두운 환경에서 발생되는 화재 탐지를 위한 모델 구조를 제안하였으며, 제안된 모델을 효과적으로 학습시키기 위한 방법도 함께 제시하였다. 제안된 방법의 효과를 평가하기 위해 학습 후 모델을 지하공동구 및 유사환경 조건의 화재 문제와 화재로 오인할 수 있는 객체를 포함한 이미지에 적용해 결과를 분석하였다. 또한 기존 딥러닝 기반 화재 탐지 모델의 정밀도, 검출률 지표와 비교함으로써 모델의 화재 탐지 성능을 정량적으로 평가하였다. 제안된 모델의 결과는 어두운 환경에서 발생되는 화재 문제에 대해 높은 정밀도와 검출률을 나타내었으며, 유사 화재 객체에 대해 낮은 오탐 및 미탐 성능을 가지고 있음을 보여주었다.

Keywords

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