An effective algorithm to detect both smoke and flame using color and wavelet analysis

Ye, Shiping; Bai, Zhican; Chen, H.; Bohush, Rykhard; Ablameyko, Sergey

doi:10.1134/s1054661817010138

Cited by 58 publications

(26 citation statements)

References 5 publications

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“…The 1-1 matching of two bits from a pair of LBP codes reflects the continuous increasing of pixel values in the corresponding direction. For example, the seventh bits of LBP codes at(2,2) and(1,3) inFig. 2dare just accordant with this situation.…”

mentioning

confidence: 70%

“…2d as an example, both the seventh bit of the centre LBP code at (2,2) and the seventh bit of its neighbouring LBP code at (1,3) are all equal to '1', so this means that local pixel values increase continuously in the seventh direction. The first bits of the LBP codes at (2,2) and (3,3) are '0' and '1', respectively. In other words, the neighbouring pixel values of the centre pixel in Fig.…”

Section: Similarity and Dissimilarity Measuresmentioning

confidence: 99%

“…2b first decrease and then increase in direction 1. A two-bit sequence from LBP codes at points (2,2) and (1,3) reflects the changes of texture in larger scales, so does the bit sequence at (2,2) and (3,3). The LBP codes at (2,2), (1,3) and 3, 3…”

Section: Similarity and Dissimilarity Measuresmentioning

confidence: 99%

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Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition

Yuan

Shi

Xia

et al. 2018

IET Computer Vision

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It is challenging to recognize smoke from visual scenes due to large variations of smoke colors, textures and shapes. To improve robustness, we propose a novel feature extraction method based on similarity and dissimilarity matching measures of Local Binary Patterns (LBP). Given two bit-sequences of an LBP code pair, the similarity and dissimilarity matching measures are defined as the ratios of the 1-1 bitwise matching number to the 0-0 bitwise matching number and the 1-0 number to the 0-1 number, respectively. To capture local code variations, we calculate the measures between LBP codes of a center pixel and its neighbors. Then we compare each measure with its global mean to propose Similarity Matching based Local Binary Patterns (SMLBP) and Dissimilarity Matching based Local Binary Patterns (DMLBP). Since SMLBP and DMLBP extract spatial variations of the 1st order LBP codes, they actually represent the 2nd order variations of pixel values. Furthermore, we adopt different mapping modes and multi-scale neighborhoods to obtain rotation and scale invariances. Finally, we concatenate the histograms of LBP, SMLBP and DMLBP to generate a feature vector containing 1st and 2nd order information. Experiments show that our method obviously outperforms existing methods.

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confidence: 70%

Section: Similarity and Dissimilarity Measuresmentioning

confidence: 99%

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Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition

Yuan

Shi

Xia

et al. 2018

IET Computer Vision

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“…Even if the RGB color model is the most straightforward in computer vision, it is not the only one used for fire-color detection. Among other color models, following three models belong: the YUV in a study published by Li et al [7], HSV model used by Vijayalakshmi et al in [8] and YCbCr one used by Ye et al in [9].…”

Section: Color Detectionmentioning

confidence: 99%

Fire Detection in Video Stream by Using Simple Artificial Neural Network

Janku

Oplatková

Dulík

et al. 2018

mendel

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This paper deals with the preliminary research of the fire detection in a video stream. Early fire detection can save lives and properties from huge losses and damages. Therefore the surveillance of the areas is necessary. Early fire discovery with high accuracy, i.e. a low number of false positive or false negative cases, is essential in any environment, especially in places with the high motion of people. The traditional fire detection sensors have some drawbacks: they need separate systems and infrastructure to be implemented, to use sensors in the case of the industrial environment with open fire technologies is often impossible, and others. The fire detection in a video stream is one of the possible and feasible solutions suitable for replacement or supplement of conventional fire detection sensors without a need for installation a huge infrastructure. The paper provides the state of the art in the fire detection. The following part of the paper proposes the new system of feature extraction and describes the feedforward neural network which was used for the training and testing of the proposed idea. The promising results are presented with over 93% accuracy on a selected dataset of movies which consist of more and highly varied instances than published by other researchers involved in the fire detection field. The structure of the neural networks promises higher computational speed than currently implemented deep learning systems.

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“…Traditional video smoke detection methods based on pattern recognition [2] and digital image processing [3] techniques depend on obtaining ample dynamic texture [4], colour features [5] [6], optical flows [7] and spatial features [8] [9]. Gubbi et al [10] adopted a pattern recognition method that manually divides the smoke video frame into 32×32 pixels to detect smoke from datasets based on wavelets [11] and support vector machines [12]. In [13], a CIELAB colour space was used to perform a smoke chromatic feature clustering method to analyse smoke colour features.…”

Section: Introductionmentioning

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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An effective algorithm to detect both smoke and flame using color and wavelet analysis

Cited by 58 publications

References 5 publications

Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition

Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition

Fire Detection in Video Stream by Using Simple Artificial Neural Network

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

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