study of pool fire heat release rate using video fire
TRANSCRIPT
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Study of pool fire heat release rate using video fire detection
Arthur Kwok Keung WONGNai Kong FONG
1
Research Centre for Fire Engineering, Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong, China
3rd International High Performance Buildings Conference, July 14-17, 2014
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Topics
1. INTRODUCTION2. LITERATURE REVIEW OF
THRESHOLD ANALYSIS METHODS
3. METHODOLOGY4. CONCLUSION5. REFERENCE
2
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INTRODUCTION
• In high performance buildings, fire and smoke spread can be affected by various factors– Geometry– Dimension– Layout– Usage of building
3
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Atrium fire
4
1.3MW
50oC
26.3m
Smokeplume
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Common fire and smoke detection method
• Point/Spot type detectors– Ionization smoke detectors– Photoelectric detectors– Heat detectors
• Line type detectors
5
The detection methods based on the use of Fire Signatures (Smoke and Heat)
Smoke detector
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Video fire detection
• The new development trend in early smoke and fire detection method– Video image processing technique
• Overcame the problems associated with conventional fire and smoke detectors– Constraints in detection distance– Area or disturbances from the surroundings
• Provided other useful information– Fire size and location of fire
• Controlled the fire spread at the early stage• Minimized the property damage 6
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Electrical and Mechanical Plant Room
7
Fire Services Pump Room
MVAC Plant Room
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Atrium
8
Hong Kong Bank
Taipei 101
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Tunnels
9
Cross HarbourTunnel
Lion Rock Tunnel
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Aircraft Cargo and Hangars
10
Aircraft Cargo
Aircraft Hangar
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Warehouses
11
Chai Wan
Kwai Chung Terminal
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Development of video fire detection
• Identification the fire source for the development of video fire detection – Spatial parameter– Spectral parameter– Temporal parameter
12Healey, G. et al., 1993. A System for Real - Time Fire Detection. IEEE, pp. 605 - 606.
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Heat release rate and Flame height
• Based on the image data of flame height, HRR of fire can be estimated
• HRR is a very important parameter in determining the smoke generation and fire severity.
• Assisted the evacuation and developed efficient smoke control systems
• HRR can be measured using mass loss rate and oxygen consumption method
13
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Heat release rate and Flame height
• Mass loss rate and oxygen consumption not feasible in practical building fire detection
• Empirical equations correlating flame height and HRR
• By using empirical equations HRR in a real building fire can be estimated by flame height by the video fire detection.
14
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Introduction of segmentation
• Various methods can be used to segment the flame image from the video images
• One of the image segmentation methods was published in 1979 call Otsu method.– Segmentation the different objects from
gray scale images using the optimum threshold value
15
Otsu, N., 1979. A Threshold Selection Method from Gray - Level Histogram. IEEE Transation Systems Man, and Cybernetics, Volume SMC-9, pp. 62 - 66.
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Segmentation
• In current study, the analysis of flame image data was conducted by modified Otsu method as the segmentation results for the flame image is not very satisfactory using the traditional Otsu method
16
Traditional Otsu method (single threshold value)
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General procedure of segmentation analysis
17
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Empirical formula estimate heat release rate from mean flame height
18
52*7.302.1 QDL
2*
DgDTcQQ
p
is the mean flame height (m), is fire pool diameter (m), is the dimensionless heat release rate (-) used in the equation, is the total heat release rate (kW), is the ambient density (kg/m3), is the specific heat of air at constant pressure (kJ/kg K), is the ambient temperature (K) is acceleration due to gravity (m/s2).
LD
*Q
Q
pc
Tg
McCaffrey equation:
Heskestad, G., 2002. Fire Plumes, Flame Height, and Air Entrainment. In: SFPE Handbook. s.l.:Society of Fire Protection Engineers, pp. 2-3.
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LITERATURE REVIEW OF THRESHOLD ANALYSIS METHODS
• Two major analysis methods are able to obtain the threshold values for image segmentation– Colour images processing analysis methods
• Threshold of red colour component
– Statistical analysis methods• Mean and Standard deviation
19
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Threshold analysis
• The threshold analysis can make use of different parameters – Fire features
• Colour• Texture• Geometry • Flicker frequency• Motion
– Discrimination of fire and non - fire– Extract the flame images
20
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Methodology• In Segmentation
– The colour images are converted to gray scale images
• RGB and YIQ colour space model is used
– The intensity histogram can be generated from the gray scale images for numerical analysis
– From the intensity level of the gray scale images, the flame region is marked as “0” and the background region is marked as “1”.
21
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Methodology
• Recognition– From the flame region of binary images, the
flame height can be estimated– By the estimation of flame height and
empirical equation of heat release rate (HRR), the pool fire HRR can be estimated
22
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Segmentation
23
Colour images
Grayscale images
Binary images
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From colour to grayscale images
24
Colour images
Grayscale images
Colourtransformation
method
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Colour transformation method
25
Colour images
Grayscale images
QIY
BGR
312.0523.0211.0322.0274.0596.0
114.0587.0299.0
yxB
yxGyxRyxY
,114.0 ,587.0 ,299.0,
YIQ colourmodel
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Schematic of RGB colour space model
26
Schematic of colour cube
B
R
G
Colourcube
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Intensity Histogram (Gray Level)
27
Gray scale images
0 255Gray Spectrum
Pixe
l Fre
quen
cy (N
umbe
r of
pix
els)
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From gray scale to binary images
28Binary images
Segmentation method
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Segmentation method
29Binary images
Multi – threshold analysis method
Rayleigh distribution based Otsu method
Modified Otsu method
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Threshold analysis
– The objective of segmentation is to extract the flame region (foreground) from the background by the Weight ( ), Mean () , Variance () and Between Class variance (BCV)
30
Calculated the Weight
Foreground:
Background:
255
1
1ki
iforeground kp
k
i
k
i
iibackground k
Nnp
0 0
Calculated the Mean
Foreground:
Background:
255
1
1ki
Tfiforeground kkip
k
ibibackground kkip
0
Calculated the Variance
Foreground:
Background:
255
1
2
kififforeground pi
k
ibibbackground pi
0
2
Calculated the Between Class Variance (BCV)
22bffbB
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Rayleigh distribution based on Otsu method
• From the calculation of mean () and variance () obtain the parameter of Rayleigh distribution ().
• Calculated the between class variance based on Rayleigh distribution
31
- Parameter of Rayleigh distribution
2;
222 fff
bbb
22bffbB
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Analysis the Max. BCV
• Based on the calculation of between class variance from traditional Otsu method and Otsu method based on Rayleigh distribution search the maximum between class variance (Max. BCV) and obtain the threshold values 32
kk
kk
BkB
BkB
2
2550
*2
2
2550
*2
max
max
Calculated the Between Class Variance (BCV)
22bffbB
Calculated the Between Class Variance (BCV)
22bffbB
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Multi – threshold analysis
• Repeat the calculation, obtained the threshold values from the last threshold values (k) to 255
• Obtained the optimum threshold value (k*) based on the pixels number of segmentation images
33
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Numerical results by modified Otsu method and segmentation images
34
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Segmentation images overlap on original colour flame images
35
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Pixel coordinates (x,y)
36
X - axisY
-ax
is
Origin (0,0) (300,0)
(0,240) (300,240)
yT
yB
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Estimate actual flame height (ho)
37
oi ddf111
Lc
Hc
Dc = do
Digital camera
22ccc HLD oc dD when
i
oio d
dhh
fddf
dyyh
o
o
oTBo size pixel
fd
dfdo
oi
Estimates image
distance Assumed: f = 2.5 in the calculation
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Estimated image flame height
38
From the specification of image device :The image size : 3.6mm (width) x 2.7mm (height)The images pixel : 300 (width) x 240 (height)The pixel size of Y – axis for calculation the flame height :
height mm 01125.0240
7.2
The pixel size : 0.01125 mm
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Fuel properties
39
2 – Propanol industrial gradeMolecular formula: C3H8OMolecular weight: 60.10 g/molCAS Registry Number: 67 – 63 –0Density: 786 g/cm3 at 20 oCHeat of combustion:(Gross value): 33.61MJ/kg(Net value): 30.68 MJ/kg. (Anon., 2014)
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Four sizes of Pool diameter
40
100mm 200mm
300mm 400mm
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Actual size of pool diameter
41
B
A
C
Typical pool diameter
100mm 200mm 300mm 400mm
A 115 197 340 410
B 54 60 65 80
C 102 197 330 410
100mm 200mm
300mm 400mm
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Experimental results of flame height analysis
42
Fuel 2 – PropanolPool diameter mm 102 197 330 410Fuel mL 100 800 800 800Mass loss rate g/s 0.1264919 0.5157317 1.6350179 2.0463882Molar coefficient kJ/mol 2220 2220 2220 2220Molar weight g/mol 60.1 60.1 60.1 60.1Heat of combustion kJ/g 36.938436 36.938436 36.938436 36.938436Combustion efficiency 0.7 0.7 0.7 0.7HRR (Based on MLR) kW 3.2706883 13.335226 42.276503 52.913266Number of images nos. 30 30 30 30Mean flame height mm 183.63 283.80 462.16 667.80Pool diameter m 0.102 0.197 0.33 0.41Revised HRR (Based on images results)
kW 1.6579322 6.1108622 21.299584 45.909748
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Experimental results
43
0
10
20
30
40
50
60
0 50 100 150 200 250 300 350 400 450
Hea
t rel
ease
rat
e (k
W)
Pool diameter (mm)
HRR (Based on MLR)
Revised HRR (Based on 30nos. flame images)
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CONCLUSIONS• Possible to predict the Heat Release
Rate (HRR) of fire– Image data– Empirical equations
• In the experimental results– Colour flame images -> Gray scale images
-> Histogram -> Segmentation• In future
– Enhance the accuracy of analysis results by the images
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REFERENCES
45
Anon., 2014. Appendix C - Fuel Properties and Combustion Data. In: SFPE Handbook of Fire Protection Engineering. United States of America: National Fire Protection Association, pp. A - 39.Borges, P. V. K., Mayer, J. & Izquierdo, E., 2008. Effective Visual Fire Detection Applied for Video Retrieval. Switzerland, EURASIP, pp. 25 - 29.Celik, T., 2010. Fast and Effective Method for Fire Detection Using Image Processing. ETRI Journal, pp. 881 - 890.Celik, T., Demirel, H. & Ozkaramanli, H., 2006. Automatic Fire Detection in Video Sequences. Italy, s.n.Celik, T., Demirel, H., Ozkaramanli, H. & Uyguroglu, M., 2006. Fire Detection in Video Sequences Using Statistical Color Model. IEEE, pp. II-213 - II-216.Chakraborty, I. & Paul , T. K., 2010. A Hybrid Clustering Algorithm for Fire Detection in Video and Analysis with Color based Thresholding Method. s.l., IEEE Computer Society, pp. 277 - 280.Chen, J., He, Y. & Wang, J., 2009. Multi - feature fusion based fast video flame detection. Building and Environment.Chen, T. H., Kao, C. L. & Chang, S. M., 2003. An Intelligent Real - TIme Fire Detection Method Based on Video Processing. IEEE, pp. 104 - 111.Chen, T. H., Wu, P. H. & Chiou, Y. C., 2004. An Early Fire - Detection Method Based on Image Processing. pp. 1707 - 1710.Cho, B. H., Bae, J. W. & Jung, S. H., 2008. Image Processing - based Fire Detection System using Statistic Color Model. s.l., IEEE Computer Society, pp. 245 - 250.Clark, R. N., 2005. [Online] Available at: http://www.clarkvision.com/articles/does.pixel.size.matter/
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Foo, S. Y., 1996. A rule - based machine vision system for fire detection in aircraft dry bays and engine compartments. Knowledge - Based Systems, Volume 9, pp. 531 - 540.Han, D. & Lee, B., 2009. Flame and smoke detection method for early real -time detection of a tunnel. Fire Safety Journal , pp. 951 - 961.Healey, G. et al., 1993. A System for Real - Time Fire Detection. IEEE, pp. 605 - 606.Heskestad, G., 2002. Fire Plumes, Flame Height, and Air Entrainment. In: SFPE Handbook. s.l.:Society of Fire Protection Engineers, pp. 2-3.Horng, W. B., Peng, J. W. & Chen, C. Y., 2005. A New Image - Based Real -Time Flame Detection Method Using Color Analysis. IEEE, pp. 100 - 105.Lai, C. L. & Yang, J. C., 2008. Advanced Real Time Fire Detection in Video Surveillance System. IEEE, pp. 3542 - 3545.Lai, C. L., Yang, J. C. & Chen, Y. H., 2007. A Real Time Video Processing Based Survillance System for Early Fire and Flood Detection. Poland, IEEE.Lee, D. & Han, D., 2007. Real - Time Fire Detection Using Camera Sequence Image in Tunnel Environment. pp. 1209 - 1220.
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Li, Z., Khananian, A., Fraser, R. H. & Cihlar, J., 2001. Automatic Detection of Fire Smoke Using Artificial Nerual Networks and Threshold Approaches Applied to AVHRR Imagery. IEEE Transactions on Geoscience and Remote Sensing,, September, Volume 39, pp. 1859 - 1870.Marbach, G., Loepfe, M. & Brupbacher, T., 2006. An image processing technique for fire detection in video images. Fire Safety Journal, pp. 285 - 289.Mohd Shafry, M. R. et al., 2012. FiLeDl Framework for Measuring Fish Length from Digital Images. International Journal of the Physical Sciences , pp. 607 - 618.Ono, T. et al., 2006. Application of neural network to analyses of CCD colour TV - camera image for the detection of car fires in expressway tunnels. Fire Safety Journal, pp. 279 - 284.Otsu, N., 1979. A Threshold Selection Method from Gray - Level Histogram. IEEE TransationSystems Man, and Cybernetics, Volume SMC-9, pp. 62 - 66.Owrutsky, J. C. et al., 2006. Long wavelength video detection of fire in ship compartments. Fire Safety Journal, pp. 315 - 320.Podrzaj, P. & Hashimoto, H., 2008. Intelligent Space as a Framwork fr Fire Detection and Evacuation. Fire Technology, pp. 65 - 76.Toreyin, B. U., Dedeoglu, Y., Gudukbay, U. & Cetin, A. E., 2006. Computer vision based method for real - time fire and flame detection. Pattern Recognition Letters, pp. 49 - 58.Wang, S. J., Jeng, D. L. & Tsai, M. T., 2009. Early fire detection mehod in video vessels. The Jurnal of Systems and Software, pp. 656 - 667.Wan, Y., Wang, J., Sun, X. & Hao, M., 2010. A Modified Otsu Image Segment Method Based on the Rayleigh Distribution. IEEE, pp. 281 - 285.Yuan, F., 2008. A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recognition Letters, pp. 925 - 932.Zhang, D. et al., 2009. Image Based Forest Fire Detection Using Dynamic Characteristics with Artificial Neural Network. s.l., IEEE Computer Soceity, pp. 290 - 293.
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Gaussian distribution (Otsu, 1979) Rayleigh distribution (Wan, et al., 2010)Background Objects Background Objects
Weight
Mean
Variance
Parameter of Rayleigh model
Between class variance
Maximum between class variance
Optimum threshold = Optimum threshold = *k
k
ii kp
00
255
11 1
kii kp
k
ii kp
00
255
11 1
kii kp
kkipk
i
i
0 00
255
1 11 1ki
Ti
kkip
kkipk
i
i
0 00
255
1 11 1ki
Ti
kkip
k
i
ipi0 0
200
255
1 1
211
ki
ipi
k
i
ipi0 0
200
255
1 1
211
ki
ipi
2
20
20
0
2
21
21
1
20110
2
211
200
2
B
TTB 20110
2
211
200
2
B
TTB
kk Bk
B2
2550
*2 max
kk Bk
B2
2550
*2 max
*k
Summarized the algorithm of Otsu and Otsu based on Rayleigh distribution