Deep smoke segmentation

Yuan, Feiniu; Zhang, Lin; Xia, Xue; Wan, Boyang; Huang, Qinghua; Li, Xuelong

doi:10.1016/j.neucom.2019.05.011

Cited by 112 publications

(66 citation statements)

References 25 publications

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“…Instead of extracting bounding boxes, Yuan et al [ 67 ] used a two-path encoder-decoder fully convolutional network (FCN) for visual smoke segmentation. FCNs can achieve end-to-end pixel-wise segmentation so the precise location of smoke can be identified in images.…”

Section: Early Fire Detection Systemsmentioning

confidence: 99%

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Barmpoutis

Papaioannou

Dimitropoulos

et al. 2020

Sensors

309

106

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The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems.

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Section: Early Fire Detection Systemsmentioning

confidence: 99%

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Barmpoutis

Papaioannou

Dimitropoulos

et al. 2020

Sensors

309

106

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“…In this section, we compare segmentation classification performance for smoke, fire and background in RGB images with different networks. We have chosen the last two best architectures for images segmentation which are U-Net network [11] and Yuan et al network [10]. We have used the same validation images not yet seen by the network to compare network performances.…”

Section: Resultsmentioning

confidence: 99%

“…Feiniu Yuan et al. [10] propose a smoke segmentation using CNN with an architecture composed of two different paths merging at the end to create the smoke mask. Both coding part are based on VGG16 architecture [8].…”

Section: Related Workmentioning

confidence: 99%

Convolutional neural network for smoke and fire semantic segmentation

Frizzi

Bouchouicha

Ginoux

et al. 2021

IET Image Processing

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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.

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“…A deep learning algorithm can extract multiclass features that are not limited to one or two typical image processing features. In [29], fully convolutional networks (FCNs) were used to realize semantic segmentation. A deep smoke segmentation network was also proposed to segment blurry smoke images via training high-quality segmentation masks.…”

Section: Related Workmentioning

confidence: 99%

Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness

Zhao¹,

Zhang²,

Zhang³

et al. 2020

Preprint

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Since smoke usually occurs before a flame arises, fire smoke detection is especially significant for early warning systems. In this paper, a DSATA(Depthwise Separability And Target Awareness) algorithm based on depthwise separability and target awareness is proposed. Existing deep learning methods with convolutional neural networks pretrained by abundant and vast datasets are always used to realize generic object recognition tasks. In the area of smoke detection, collecting large quantities of smoke data is a challenging task for small sample smoke objects. The basis is that the objects of interest can be arbitrary object classes with arbitrary forms. Thus, deep feature maps acquired by target-aware pretrained networks are used in modelling these objects of arbitrary forms to distinguish them from unpredictable and complex environments. In this paper, this scheme is introduced to deal with smoke detection. The depthwise separable method with a fixed convolution kernel replacing the training iterations can improve the speed of the algorithm to meet the enhanced requirements of real-time fire spreading for detecting speed. The experimental results demonstrate that the proposed algorithm can detect early smoke, is superior to the state-of-the-art methods in accuracy and speed, and can also realize real-time smoke detection.

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Deep smoke segmentation

Cited by 112 publications

References 25 publications

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Convolutional neural network for smoke and fire semantic segmentation

Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness

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