1. Detection and Measurement of Pavement Cracking Bagas Prama Ananta

2. Overview Background Aims The Proposed Method Tests and Results Conclusion Future Work

3. Background Roads are a major asset in most countries To manage these assets, road authorities need:  Accurate, up-to-date information on the condition of their road network Information on defects is vital to keeping a well maintain road network

4. Why do we do a Road Maintenance? Early detection of defects in road surfaces helps:  maintenance to be performed before defects develop into more serious problems, such as potholes and pop- outs. Thus, detection and measurement of pavement cracking:  Provide valuable information on the condition of a road network  Reduce maintenance cost  Create a better road network for people to use

5. Types of Cracks Transverse Cracking

6. Types of Cracks… Longitudinal Cracking

7. Types of Cracks Crocodile Cracking

8. Background… The 1 st maintenance process is the detection of defects Once detected, defects can be analysed and a decision can be made as to what action needs to be taken

9. Present Method Visual inspection  Two operators travel at 20 km/h  One as the driver, another to record the defect  Time consuming, costly and can be dangerous

10. Present Method… An improved method  A video based system  Able to record the pavement up to 100 km/h  The recorded video is then inspected off-line at speed of 20 km/h

11. Present Method…

12. Project Aims Proposing a method of semi-automated detection of cracking defects in the road pavement from video footage. Advantages of a semi-automated system:  Faster  More reliable  More accurate

13. Challenges Low resolution of the captured image  768x576 pixels or 0.44 megapixels Lossy compression is used  To make storage of the data practical Highly variable lightning conditions Potential false identification of cracks  Shadows, rail and tram tracks, other road objects

14. Challenges Sample set provided by PureData, however the images were not suitable for testing.  Resolutions are too low  Most images are not sharp (i.e. a lot of blurry images) which result in noises 1200x900 (~1mp) images are used to test the method

15. Commercial Implementation Several companies offer solutions for monitoring road surface condition Such solution are the CSIRO and Roadware crack detection systems Due to the commercial nature, information on their operation is limited

16. CSIRO’s Road Crack Detection Vehicle Comprised of mostly custom designed and manufactured hardware The system is very expensive and requires specialised maintenance

17. CSIRO’s Road Crack Detection Performs all data analysis in the field No image data is kept The only output is the road quality report Leads to uncertainty with the accuracy of the results Further manual inspection is needed to guarantee the results of the systems

18. Roadware’s Wisecrax Performs all data analysis off-line Dual video cameras record 1.5 m by 4 m sections of pavement High intensity strobe lights produce consistent illumination of pavement images

19. Solution to Similar Problems Crack Detection by the use of a laser based system Work on this problem was commenced by a previous honours student (Timothy Evans).  A modified watershed algorithm was proposed  Difficulty in testing his algorithm  This project uses part of Tim’s method for detecting cracks Sun et. al [2] proposed a new segmentation algorithm for detecting tiny objects  Edge detection, line growing and line cutting Crack detection based on the “grid-cell” analsyis by Xu and Huang

20. The Proposed Method To use image processing techniques to segment the cracking information.  Seed Selection  Line growing  Noise removal

21. Pipeline of Solution

22. Initial Detection or Seeding Horizontal and Vertical Scan Contrast Comparison Combine seed

23. Profiles of Cracks, Lane Marks and Shadows The challenge in crack detection is to differentiate between cracks and noises, where noises are:  Stone texture  Leaves, branches, etc  Lane Markings  Shadows Analysing the different between the profiles between cracks and noise (lane marks and shadows) is useful for segmenting the crack from images.

24. A Lane Mark profile

25. A Shadow Profile

26. A Crack Profile

27. Cracks on Shadows Cracks Cracks on shadows

28. Seed Selection - Horizontal and Vertical Scan

29. Original Image

30. Horizontal and Vertical Pass

31. Seed Selection – Contrast Comparison A represents the current pixel. B and C are the candidate pixel for growing. Calculate all the 4 directions: R=max(R(a), R(b), R(c), and R(d)). If R > T, then the seed is validated else seed is discarded

32. Original Image

33. Contrast Selection

34. Seed Selection - Combination The proposed method of seed selection The combination of Horizontal & Vertical and Contrast comparison More accurate

35. Original Image

36. Horizontal and Vertical Pass & contrast Selection

37. Line Growing – Watershed transformation current pixel Start from the current pixel Mark pixels that are similar to the current pixel as a potential crack seed

38. Original Image

39. Watershed Transformation / Line Growing Algorithm

40. Noise Removal Flooded points must not be too close with each other to the extent the area is overcrowded A crack will generally have a certain width A crack will generally not be an isolated pixel

41. Original Image

42. Noise Removal – Over Crowded

43. Original Image

44. Noise Removal – Isolated Pixels and Crack Width

45. Original Image

46. Horizontal and Vertical Pass

47. Contrast Selection

48. Horizontal and Vertical Pass & contrast Selection

49. Watershed Transformation / Line Growing Algorithm

50. Noise Removal – Over flooding

51. Noise Removal – Isolated Pixels and Crack Width

52. Currently using a global threshold to determine the seeds During seed selection varying lightning condition make the selection of a global threshold difficult Solution: a localised threshold method is proposed Inconsistent Lightning Condition

53. Original Image

54. Result using a global threshold

55. Result using a localised threshold

56. Original Image

57. Result using a global threshold

58. Result using a localised threshold

59. Original Image A Result of Image A Original Image B Result of Image B

60. Original Image - Horizontal Crack

61. Result of the original image - Horizontal Crack

62. Original Image

63. Result of the Original Image

64. Original Image

65. Result of the Original Image

66. Test and Results The algorithm is tested over 123 images Images of pavement containing cracks (67)  95.5% of successful detection  4.5 % of false detection (due to inconsistent lightning) Images of pavement containing no cracks (56)  64% of successful non detection  36% of false identification of crack (due to road edges, shadows on leaves and stick)

67. An pavement image containing no cracks

68. Result of the Original Image

69. An pavement image containing no cracks

70. Result of the Original Image

71. Conclusion and Future Work Project Result:  A semi-automated crack detection system  Works with 1megapixel images  Achieve 81.3% of success  Achieve 18.7% of failure More work on the seed selection process using a localised threshold Test other techniques for noise removal:  Supervised Learning – recognition of crack and noise patterns

72. Any Question?