Abstract
In this paper, we propose a novel approach to fire 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(http://www.fit.vutbr.cz/research/view_pub.php.cs?id=12124) will facilitate further development of fire detection and segmentation methods, and that the methods should be based on general purpose segmentation networks.
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Notes
- 1.
Deeplab v3 TensorFlow re-implementation - https://github.com/rishizek/tensorflow-deeplab-v3.git.
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Acknowledgements
The work presented in this paper was supported by the V3C - Visual Computing Competence Center, funded by the Technology Agency of the Czech Republic under the TE01020415 project.
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Mlích, J., Koplík, K., Hradiš, M., Zemčík, P. (2020). Fire Segmentation in Still Images. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_3
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