Publication detail

Fire Segmentation in Still Images

MLÍCH, J. KOPLÍK, K. HRADIŠ, M. ZEMČÍK, P.

Original Title

Fire Segmentation in Still Images

Type

conference paper

Language

English

Original Abstract

In this paper, we propose a novel approach to fire localization in images based on a state of the art semantic segmentation method DeepLabV3. We compiled a data set of 1775 images containing fire from various sources for which we created polygon annotations. The data set is augmented with hard non-fire images from SUN397 data set. The segmentation method trained on our data set achieved results better than state of the art results on BowFire data set. We believe the created data set will facilitate further development of fire detection and segmentation methods, and that the methods should be based on general purpose segmentation networks.

Keywords

Fire detection, Semantic segmentation, Deep learning, Neural Networks, Emergency situation analysis

Authors

MLÍCH, J.; KOPLÍK, K.; HRADIŠ, M.; ZEMČÍK, P.

Released

10. 2. 2020

Publisher

Springer International Publishing

Location

Auckland

ISBN

978-3-030-40605-9

Book

Springer International Publishing

Edition

Lecture Notes in Computer Science

Pages from

27

Pages to

37

Pages count

11

URL

https://link.springer.com/chapter/10.1007%2F978-3-030-40605-9_3

BibTex

@inproceedings{BUT162094,
  author="Jozef {Mlích} and Karel {Koplík} and Michal {Hradiš} and Pavel {Zemčík}",
  title="Fire Segmentation in Still Images",
  booktitle="Springer International Publishing",
  year="2020",
  series="Lecture Notes in Computer Science",
  pages="27--37",
  publisher="Springer International Publishing",
  address="Auckland",
  doi="10.1007/978-3-030-40605-9\{_}3",
  isbn="978-3-030-40605-9",
  url="https://link.springer.com/chapter/10.1007%2F978-3-030-40605-9_3"
}