Fire Segmentation in Still Images

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.