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An image based disdrometer verification and raindrop analysis

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Using high speed camera to catch raindrops and do analysis on them to predict rainrate

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An image based disdrometer verification and raindrop analysis

  1. 1. An Image-based Disdrometer Verification and Raindrop Analysis 影像式雨滴譜儀系統驗證與雨滴分析 指導教授: 鐘太郎 教授 學生: 102061539 黃政翰
  2. 2. Outline  Introduction  Raindrop Analysis Theories  Structure of the Proposed Disdrometer and Software Algorithm  Result of Experiments  Conclusion
  3. 3. Introduction
  4. 4. Precipitation Observation  Weather forecasting must tell the information of precipitation  Behavior of rainfall phenomena are due to local and sudden precipitation  Predicting rainfall intensity  Advance preparation can prevent potential disasters from happening
  5. 5. Observing Systems  Primitive method  Collect drops by a box with dye paper  Wasting time and low efficiency  Radar and Satellite sensors  Collect data in large region  Not accurate enough in analyzing rather small region  Disdrometers  Analyze raindrop particles
  6. 6. Why Disdrometer?  Increase the accuracy of the raining condition in a small region  Increase the accuracy of the radar measurement  Raindrop feature analysis can be used in  Air traffic control  Scientific examination  Weather observation system
  7. 7. Types of Disdrometers  Joss-Waldvogel Disdrometer  Acoustic Disdrometer  Optical Particle Size Velocity Disdrometer  2-D Video Disdrometer  Image-based Disdrometer
  8. 8. Joss-Waldvogel Disdrometer  Discriminate drop size by receiving the impact kinetic energy  It cannot determine the drop shape  Low sensitivity in drizzling and heavy rain
  9. 9. Acoustic Disdrometer  Drops hitting on the sensor induces sonic wave  The piezoelectric sensor can measure the rainfall intensity  It does not provide raindrop distribution data  Wind influences the measurement easily
  10. 10. Optical Particle Size Velocity Disdrometer  A row of laser beam points to a sensor  Measure drop size by calculating the duration of light extinction  2 dimension is possible; speed measurement is available  Drop mismatch makes errors
  11. 11. 2-D Video Disdrometer  Using 2 line-scan cameras to measure size and shape  Velocity can be calculated by traveling time and distance between two frames  Slanted particle falling path makes image distortion and lead to errors
  12. 12. Image-based Disdrometer  Use CCD camera to capture raindrops  Double exposures in each frame  Too many drops in one image influences matching accuracy a lot
  13. 13. Disdrometer Comparison Disdrometer Type Measuring Mechanics Advantages Disadvantages Joss-Waldvogel Falling impact Good performace in small size variation Poor at measuring drops that are too small or too large Acoustic Inducing sonic wave Good at monitoring large size drops Wind influence easily OTT Parsivel Make drops falling through a laser beam With good accuracy in measuring drop size Drop mismatch and near drops makes error 2D Image Use 2 line-scan camera to measure size and shape Size, shape and velocity measurement are available Slanted falling drops are distorted Image-based use CCD camera to capture images and find parameters by image processing Low cost of recording and flexible system setting Camera frame rate and resolution influence the result
  14. 14. Raindrop Analysis Theories
  15. 15. Drop Size Distribution (DSD)  Marshall and Palmer (1948) [11]  Announced that DSD can be described by an exponential distribution D D eNN   0 4 0 08.0   cmN 121.0 41   cmR
  16. 16. Drop Size Distribution (DSD)  The relationship comes up with errors in small drops  Ulbrich (1983) [12]-> Gamma function  are the parameters D D eDNN    0 ,, 0N
  17. 17. Drop Size Distribution (DSD)  The relationship comes up with errors in small drops  Feingold and Levin (1986) [13]-> Lognormal function  are the parametersTg ND ,,
  18. 18. DSD is related to  DSD has some relations with rain rate and rain types  Kozu and Nakamura (1991) [14]  DSD is related to reflectivity factor measured by radar  Doviak and Zrnic (1984) [15]  DSD can calibrate and increase the accuracy of the radar  Joss and Waldvogel (1969) [16]
  19. 19. Drop Velocity  Gunn and Kinzer (1949) [17]  Experiments of raindrop terminal velocities through stagnant air  Battan (1964) [18]  Experiments in thunderstorm  Foote and DuToit (1969) [19], Beard (1976) [20]  Experiments in different air density
  20. 20. Drop Velocity Different velocity curve under different conditions Different velocity curve under different air density Atlas et al. [5] Mitchell [21] Beard [20]
  21. 21. Structure of the Proposed Disdrometer and Software Algorithm
  22. 22. System Structure  Optical Unit  Light source (Part A)  Image Acquisition Unit  Lens (Part B)  CCD camera (Part C)  Data Processing Unit  Processing Algorithm (Part D) System Structure Diagram
  23. 23. System Structure System Structure Photograph
  24. 24. Optical Unit  Viswell HBL-100  Uniform blue LED light source  Bring up light intensity  Enhance image contrast
  25. 25. Optical Unit  Relative position between light source and camera  It depends on the lens used in the system  Using a telecentric lens  We can only put the light source facing to the camera
  26. 26. Optical Unit Light source on the side Light source facing to the camera
  27. 27. Optical Unit  Proper adjustment of light intensity Light Intensity 50 52.5 55 57.5 Contrast under 0 degree light 0.241 0.232 0.053 0
  28. 28. Image Acquisition Unit: Camera  CCD Camera: Pylon Basler Aca640-90gm  Monochrome, adjustable gain and exposure time  High frame rate (90 fps in stable)  659 pixels * 494 pixels  SDK is provided
  29. 29. Image Acquisition Unit: Lens  Lens: OPTO Engineering TC13064  Telecentric lens  Minimizes blur effect  Long depth of field
  30. 30. Image Acquisition Unit: Lens  Compare with the old one: Computar M1214-MP2 TC13064 M1214-MP2 FOV 6.5cm*4.8cm 5cm*3.7cm DoF about 15cm about 3~5cm Focus Fixed Manual adjustable Iris Fixed Manual adjustable Out of focus Blurriness Slight Severe
  31. 31. Image Acquisition Unit: Lens Image taken by M1214-MP2 Image taken by TC13064
  32. 32. Data Processing Unit  Software platform: Visual Studio 2012, using Visual C++  Combined with: OpenCV 2.4.9, Pylon 4 SDK, Matlab2010
  33. 33. Data Processing Unit: Camera Parameter Setting  Done before taking every set of images  Critical parameters  Image size  Exposure time  Gain  Recording duration
  34. 34. Data Processing Unit: Camera Parameter Setting Set the duration time of recording or the number of images taken Set the exposure time Set the gain Set the image size Start taking images Start the program
  35. 35. Data Processing Unit: Drop Extraction and Analysis
  36. 36. Drop Extraction and Analysis: Make a Background  Background calculation: average all the frames  is the ith frame, is the number of taken images k if Bg k i   ][ ][if k k average
  37. 37. Drop Extraction and Analysis: Make a Background Original image Image without background
  38. 38. Drop Extraction and Analysis: Image Binarization  Median Filter 5*5 -> reduce noise  Choose proper threshold method  Depend on the image we get  Max Entropy, Iterative, Otsu, Region Growing, Level Set had been tried  100us exposure time image: Max Entropy Thresholding  2000us exposure time image: Iterative Thresholding
  39. 39. Drop Extraction and Analysis: Max Entropy Thresholding  The threshold determines by maximizing the entropy of foreground and background  is the gray-level probability density function for the image ) )( )( log( )( )(255 1 Tq ip Tq ip H fTi f f   ) )( )( log( )( )( 0 Tq ip Tq ip H b T i b b   )(ip
  40. 40. Drop Extraction and Analysis: Max Entropy Thresholding  and are the probabilities that a given pixel belongs to foreground or background when the threshold is   255 1 )()( Ti f ipTq   T i b ipTq 0 )()( )(Tqf )(Tqb T )max( bf HHT 
  41. 41. Drop Extraction and Analysis: Iterative Thresholding  An initial threshold is chosen, typically the average intensity of the image  Mean gray value of foreground and background are calculated  is the gray-level probability density function for the image T   255 1 )( Ti f iip   T i b iip 0 )( 2 bf T    )(ip
  42. 42. Drop Extraction and Analysis: Binary images after thresholding 100us binary image 2000us binary image
  43. 43. Data Processing Unit: Drop Extraction and Analysis
  44. 44. Drop Extraction and Analysis: Contour Finding  Binary images have high contrast  Easy to determine edges
  45. 45. Drop Extraction and Analysis: Ellipse Fitting
  46. 46. Drop Extraction and Analysis: Minimal Bounding Rectangle
  47. 47. Drop Extraction and Analysis: Unsuitable Objects Elimination  Out of bound elimination  Any contour touches the border are eliminated  100us images  Axis ratio 0.4~1.2 -> treated as raindrops  2000us images  Eliminate if the width is larger than height
  48. 48. Drop Extraction and Analysis: Size Calculation 1.01.0  PA 2 1 )(2  A Dm  mm1.0 mm1.0 P
  49. 49. Drop Extraction and Analysis: Velocity Calculation 2 2 w hd  tdv / h w 2 w 2 w
  50. 50. Drop Extraction and Analysis: DSD calculation  According to Liu et al. (2013)  : DSD  : number of drops of each bin  : bin interval (1mm)  : Sampling volume of the drop-falling space )(DN )( 13  mmm )(DNum dD )(15.0 3 mWHV  m m m dDV DNum DN   Pr )( )( framemymx osuremyosuremx tvv tvhHtvwW    )()( Pr expexp
  51. 51. Drop Extraction and Analysis: Rain Rate Calculation  : velocity of the measured diameter    0 3 )( 6 mmmm dDvDDNR  mv
  52. 52. Result of Experiments
  53. 53. Marble Experiments  Throwing marbles from 1mm to 5mm separately  100us exposure time, 300 gain, maximum light  3 seconds duration, 270 frames, as one set of images
  54. 54. Marble Experiments Measured diameter vs Calculated Diameter Measured diameter vs Measured Area
  55. 55. Marble Experiments Theoretical Value Average Std Min Max Error diameter(100us) (mm) 0.25 0.3110 0.0902 0.0707 0.4998 24.3815 area(100us) (mm 2 ) 0.0491 0.1317 0.0511 0.055 0.215 168.1602 Theoretical Value Average Std Min Max Error diameter(100us) (mm) 1 1.0219 0.2217 0.5 1.4948 2.1872 area(100us) (mm 2 ) 0.785 0.7369 0.4036 0.09 1.9 6.1687 Theoretical Value Average Std Min Max Error diameter(100us) (mm) 2 2.0560 0.2808 1.5 2.4749 2.8018 area(100us) (mm 2 ) 3.142 3.2074 1.195 0.535 5.305 2.0950 Theoretical Value Average Std Min Max Error diameter(100us) (mm) 3 3.0887 0.2514 2.5 3.4883 2.9583 area(100us) (mm 2 ) 7.069 7.4855 1.5203 1.62 10.735 5.8988 Theoretical Value Average Std Min Max Error diameter(100us) (mm) 4 4.0374 0.2346 3.5 4.4721 0.9339 area(100us) (mm 2 ) 12.566 12.4727 2.2965 4.225 17.085 0.7456 Theoretical Value Average Std Min Max Error diameter(100us) (mm) 5 4.9206 0.3012 4.5 5.4447 1.5886 area(100us) (mm 2 ) 19.635 18.1703 3.5536 5.985 24.27 7.4594
  56. 56. Marble Experiments  Overlapping leads to the presence of outliers  Larger marbles have higher error  Axis ratio recognition gives larger range to be distinguished in large size objects  Small marbles error  Some noises are remained after thresholding
  57. 57. Water Sprinkling Experiments  Spread water by sprinkler  100us exposure time, 300 gain, maximum light  2000us exposure time, 300 gain, half light  5 seconds duration, 450 frames, as one set of images
  58. 58. Water Sprinkling Experiments Measured diameter vs Calculated Diameter Measured diameter vs Measured Area
  59. 59. Water Sprinkling Experiments Axis ratio distribution
  60. 60. Water Sprinkling Experiments Diameter vs Speed Canting angle histogram
  61. 61. Water Sprinkling Experiments Theoretical Value Average Std Min Max Error diameter(2000us) 0.25 0.324 0.096 0.045 0.500 29.7393 speed(2000us) 0.7847 0.643 0.530 0.069 2.705 18.0382 diameter(100us) 0.25 0.456 0.045 0.300 0.499 82.2501 area(100us) 0.0491 0.136 0.045 0.005 0.235 177.8743 Theoretical Value Average Std Min Max Error diameter(2000us) (mm) 1 0.9795 0.2773 0.5 1.4997 2.0474 speed(2000us) (m/s) 3.9972 2.1910 0.8306 0.05 8.6185 45.1867 diameter(100us) (mm) 1 0.7777 0.1927 0.5 1.4977 22.2321 area(100us) (mm2 ) 0.7854 0.4596 0.2566 0.02 1.795 41.4878 Theoretical Value Average Std Min Max Error diameter(2000us) (mm) 2 1.8524 0.2785 1.5 2.4989 7.3819 speed(2000us) (m/s) 6.5477 3.4311 1.1124 0.1 10.454 47.5979 diameter(100us) (mm) 2 1.7033 0.2171 1.5 2.4660 14.8353 area(100us) (mm2 ) 3.1416 1.6046 0.5596 0.475 3.685 48.9244 Theoretical Value Average Std Min Max Error diameter(2000us) (mm) 3 2.8973 0.2750 2.5 3.4985 3.4220 speed(2000us) (m/s) 7.9474 4.3873 1.4764 0.75 10.8093 44.7961 diameter(100us) (mm) 3 2.8418 0.0189 2.82843 2.8552 5.2725 area(100us) (mm 2 ) 7.0686 2.97 0.9334 2.31 3.63 57.9831 Theoretical Value Average Std Min Max Error diameter(2000us) (mm) 4 3.8909 0.2694 3.5 4.4933 2.7287 speed(2000us) (m/s) 8.7156 5.1360 1.9515 0.75 12.8701 41.0717 diameter(100us) (mm) 4 N/A N/A N/A N/A N/A area(100us) (mm2 ) 12.5664 N/A N/A N/A N/A N/A Theoretical Value Average Std Min Max Error diameter(2000us) (mm) 5 4.9799 0.2777 4.5 5.4811 0.4011 speed(2000us) (m/s) 9.1372 5.9424 2.1716 1.4 11.4378 34.9649 diameter(100us) (mm) 5 N/A N/A N/A N/A N/A area(100us) (mm2 ) 19.6350 N/A N/A N/A N/A N/A Average Std Min Max canting angle -35.6758 26.6744 -180 0
  62. 62. Water Sprinkling Experiments  Larger error of speed difference in larger drop size  Overlapping issue  Sprinkled water are not in terminate velocity  Few drops are grabbed in this size interval
  63. 63. Raining Experiments  Real raining condition at 17:00, 27 Aug 2015 at Hsinchu, Taiwan  5 seconds duration, 450 frames, as one set of images, 30 seconds in total  2700 images taken in 100us and 2000us respectively
  64. 64. Raining Experiments Histogram of size distribution Axis ratio distribution
  65. 65. Raining Experiments Measured diameter vs Calculated Diameter Measured diameter vs Measured Area
  66. 66. Raining Experiments Histogram of size distribution Histogram of canting angle distribution
  67. 67. Raining Experiments Diameter vs Speed
  68. 68. Raining Experiments Theoretical Value Average Std Min Max Error diameter(2000us) (mm) 0.25 0.374864 0.108827 0.14 0.497947 49.9457 speed(2000us) (m/s) 0.7847 1.15072 0.598629 0.28 3.2 46.6446 diameter(100us) (mm) 0.25 0.441055 0.065593 0.31305 0.494975 76.4218 area(100us) (mm2 ) 0.0491 0.137188 0.060991 0.025 0.215 179.4043 Theoretical Value Average Std Min Max Error diameter(2000us) (mm) 1 0.9394 0.2447 0.5 1.4999 6.0578 speed(2000us) (m/s) 3.9972 2.6945 0.8629 1.05 5.3062 32.5902 diameter(100us) (mm) 1 0.8658 0.2196 0.5 1.4863 13.4154 area(100us) (mm2 ) 0.7854 0.6174 0.3419 0.055 1.87 21.3847 Theoretical Value Average Std Min Max Error diameter(2000us) (mm) 2 1.7479 0.1486 1.5402 2.0803 12.6055 speed(2000us) (m/s) 6.5477 5.3268 0.5195 4.0784 6.0691 18.6465 diameter(100us) (mm) 2 1.9637 0.1418 1.5 2.2030 1.8129 area(100us) (mm 2 ) 3.1416 3.0464 0.3600 1.695 3.695 3.0294 Average Std Min Max canting angle -10.276 29.3070 -180 -0.273
  69. 69. Raining Experiments  Small raindrops dominant  Small canting angle -> almost no wind  Speed are lower than theoretical value  Image processing leads to the error  Raining condition difference
  70. 70. Raining Experiments: Image Processing Error  If there is one-pixel error in width  An 1mm raindrop is in 10% error  Theoretical velocity is in 9% error
  71. 71. Raining Experiments: Image Processing Error
  72. 72. Raining Experiments  According to the statistical data of Central Weather Bureau  Rain rate = 0.5 mm/h  Wind speed = 0.3 m/s  The calculated data  Rain rate = 0.5721 mm/h, Error = 14.42%  Wind speed = 0.2095 m/s, Error = 30.01%
  73. 73. Conclusion
  74. 74. Conclusion  We have built an image-based disdrometer:  Low cost and Easy-assembling  Results are in the tendency of the empirical formula  Keep good performance in windy situation  Three kinds of experiments were done to verify the system  The structure and processing procedures are feasible  Thresholding calibration is needed  Calculated rain rate is in the error around 15%
  75. 75. Future Work  Still need further calibration in every set of images to increase measurement accuracy  Increasing FOV or frame rate to increase capture probability  Improve contrast in field experiment  Overlapping issue -> Set 2 CCD camera to make 3D images

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