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

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

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728119960
DOI
出版ステータスPublished - 2018 11 27
イベント2018 International Conference on Information and Communication Technology Robotics, ICT-ROBOT 2018 - Busan, Korea, Republic of
継続期間: 2018 9 62018 9 8

Other

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

ASJC Scopus subject areas

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

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