Fire Area Detection based on Convolutional Neural Network and Improved A∗ Path Planning

Jia Hua Yu, Shin Nyeong Heo, Ji Sun Shin, HeeHyol Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this research, a new fire detection method and a new path planning algorithm are proposed. Traditional fire detection methods are based on fixed RGB models and they are not accurate enough under different circumstances. Traditional A∗ path planning algorithm always focuses on a minimum cost from a start point to an end point, so it is not suitable for complex environment. To solve fire detection problem, we use an object detection algorithm based on a convolutional neural network and trained it with real fire images to detect fire area in an image. For the path planning problem, an improved A∗ algorithm with new weight for different area and box blur method are used to ensure the output path is away from the obstacle. In the end of this paper, we simulate disaster environment in Unity3D and implement two algorithms to measure their performance.

Original languageEnglish
Title of host publication2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119960
DOIs
Publication statusPublished - 2018 Nov 27
Event2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018 - Busan, Korea, Republic of
Duration: 2018 Sep 62018 Sep 8

Other

Other2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018
CountryKorea, Republic of
CityBusan
Period18/9/618/9/8

Keywords

  • Convolution neural network
  • first responder
  • Object detection
  • Path planning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Communication

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