Detection and Measurement of Pavement Cracking

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 1st 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