Researching | Review on Smoke Detection Algorithms for Video Images

Laser & Optoelectronics Progress, Volume. 58, Issue 4, 0400003(2021)

Review on Smoke Detection Algorithms for Video Images

Changyou Chen and Jiansheng Yang^*

Author Affiliations

College of Electrical Engineering, Guizhou University, Guiyang, Guizhou 550025, China

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Figures&Tables (8)

References (95)

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Figures & Tables(8)

Fig. 1. Flow chart of smoke detection for video images

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Fig. 2. Discrete diagram for estimating smoke motion direction ^[52]

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Fig. 3. Examples for smoke detection under different scenes^[15]

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Fig. 4. Smoke detection based on multi-feature fusion

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Table 1. Formulas for calculating statistical metric parameters of texture features
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Table 1. Formulas for calculating statistical metric parameters of texture features
Metric parameter Formula
Mean μ= $\frac{1}{N_{1}} \overset{N_{1} - 1}{\sum_{a = 0}}$ x₁(a)
Uniformity U= $\overset{N_{1} - 1}{\sum_{a = 0}} x_{1}^{2}$ (a)
Entropy E=- $\overset{N_{1} - 1}{\sum_{a = 0}}$ Q_a(ln Q_a)
Standard deviation σ= $\sqrt[]{\frac{1}{N_{1}} \overset{N_{1} - 1}{\sum_{a = 0}} {[x_{1} (a) - μ]}^{2}}$

Table 2. Statistical parameters for characterizing GLCM^[32]
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Table 2. Statistical parameters for characterizing GLCM^[32]
Parameter Formula
Energy A_ASM= $\sum_{e} \sum_{f}$ P(e,f)²
Contrast C_CON= $\sum_{e} \sum_{f}$ (e-f)²P(e,f)
Correlation C_COR= $\frac{\sum_{e} \sum_{f} (e -) (f -) P (e, f)}{σ_{x} σ_{y}}$
Homogeneity I_IDM= $\sum_{e} \sum_{f} \frac{1}{1 + {(e - f)}^{2}}$ P(e,f)

Table 3. Smoke detection algorithm for video images

Table 3. Smoke detection algorithm for video images

Method	Pre-processing	Smoke feature	Classification	Accuracy/%	Falsepositive/%	Falsenegative/%
Method inRef. [57]	Color segmentation technique	Texture, energy, and motion	Neural network(NN)	80	-	-
Method inRef. [15]	Color segmentation technique	Energy and texture	SVM	96.29	3.71	1.74
Method inRef. [5]	Motion segmentation technique	Color , fuzzy feature, shape irregularity, and motion	PSO-SVM	97.16	2.84	-
Method inRef. [52]	Motion segmentation technique	Color, texture, energy, HOG feature, shape irregularity, and motion	AdaBoost-RBFSVM	91.25	0.31	-
Method inRef. [67]	Motion segmentation technique	Shape irregularity, diffusion property, and texture	ANFIS-Bayes-MR	99.09	1.94	-

Table 4. Smoke detection results for video images based on different deep learning network models

Table 4. Smoke detection results for video images based on different deep learning network models

Reference	Network model	Accuracy/%	False positive/%
Method in Ref. [89]	Deep normalization and CNN(14 layers)	97.52	0.6
Method in Ref. [90]	RCNN+3D CNN	95.23	0.39
Method in Ref. [91]	CNN(AlexNet)	99.44	0.44
Method in Ref. [92]	CaffeNet CNN	98	1.7
Method in Ref. [93]	DN-CNN	96.37	0.60
Method in Ref. [87]	YOLOv2	94.7	5.2
Method in Ref. [87]	GMM+YOLOv2	98.1	0.8

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Changyou Chen, Jiansheng Yang. Review on Smoke Detection Algorithms for Video Images[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0400003

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Paper Information

Category: Reviews

Received: Jun. 17, 2020

Accepted: Aug. 7, 2020

Published Online: Feb. 8, 2021

The Author Email: Yang Jiansheng (jsyang3@gzu.edu.cn)

DOI:10.3788/LOP202158.0400003

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology