Abstract
In recent decades, global warming has contributed to an increase in the number and intensity of wildfires destroying millions hectares of forest areas and causing many casualties each year. Firemen must therefore have the most effective means to prevent any wildfire from breaking out and to fight the blaze before being unable to contain and extinguish it. This article will present a new network architecture based on Convolutional Neural Network to detect and locate smoke and fire. This network generates fire and smoke masks in an RGB image by segmentation. The purpose of this work is to help firemen in assessing the extent of fire or monitor an incipient fire in real time with a camera embedded in a vehicle. To train this network, a database with the corresponding images and masks has been created. Such a database will allow to compare the performances of different networks. A comparison of this network with the best segmentation networks such as U-Net and Yuan networks has highlighted its efficiency in terms of location accuracy, reduction of false positive classifications such as clouds or haze. This architecture is also efficient in real time.
Highlights
Each year, the news highlights the importance of fire detection when it comes to saving lives, wild forests and homes
Yann Le Cun pointed out the use of Convolutional Neural Network (CNN) for classification in image learning
The paper is organized as follows: In Section 2, first of all, we have reviewed related work to convolutional neural network applied to semantic segmentation as well as the evolution of smoke and fire detection techniques
Summary
The news highlights the importance of fire detection when it comes to saving lives, wild forests and homes. Yann Le Cun pointed out the use of Convolutional Neural Network (CNN) for classification in image learning. CNN’s enhancements relate to the classification of images and to the location of objects whose bounding box methods are examples [3,4,5] Kaiming He et al combine bounding box and segmentation to improve the object localization [6]. We suggest studying and comparing different convolutional– deconvolutional architectures of neural network segmentation to detect and locate smoke and fire in RGB frames. The paper is organized as follows: In Section 2, first of all, we have reviewed related work to convolutional neural network applied to semantic segmentation as well as the evolution of smoke and fire detection techniques. The last section summarizes our work and lists the ways to improve semantic segmentation of smoke and fire
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