Publication Details
Fire Segmentation in Still Images
Koplík Karel, Ing.
Hradiš Michal, Ing., Ph.D. (DCGM)
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)
Fire detection, Semantic segmentation, Deep learning, Neural Networks, Emergency
situation analysis
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.
@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"
}