detection and measurement of pavement cracking bagas prama ananta
TRANSCRIPT
Detection and Measurement of Pavement Cracking
Bagas Prama Ananta
Overview
Background Aims The Proposed Method Tests and Results Conclusion Future Work
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
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
Types of Cracks
Transverse Cracking
Types of Cracks…
Longitudinal Cracking
Types of Cracks
Crocodile Cracking
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
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
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
Present Method…
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
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
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
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
CSIRO’s Road Crack Detection Vehicle
• Comprised of mostly custom designed and manufactured hardware• The system is very expensive and requires specialised maintenance
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
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
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
The Proposed Method
To use image processing techniques to segment the cracking information. Seed Selection Line growing Noise removal
Pipeline of Solution
Initial Detection or Seeding
Horizontal and Vertical Scan Contrast Comparison Combine seed
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.
A Lane Mark profile
A Shadow Profile
A Crack Profile
Cracks on Shadows
Cracks
Cracks on shadows
Seed Selection - Horizontal and Vertical Scan
Original Image
Horizontal and Vertical Pass
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
Original Image
Contrast Selection
Seed Selection - Combination The proposed method of seed selection The combination of Horizontal & Vertical
and Contrast comparison More accurate
Original Image
Horizontal and Vertical Pass & contrast Selection
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
Original Image
Watershed Transformation / Line Growing Algorithm
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
Original Image
Noise Removal – Over Crowded
Original Image
Noise Removal – Isolated Pixels and Crack Width
Original Image
Horizontal and Vertical Pass
Contrast Selection
Horizontal and Vertical Pass & contrast Selection
Watershed Transformation / Line Growing Algorithm
Noise Removal – Over flooding
Noise Removal – Isolated Pixels and Crack Width
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
Original Image
Result using a global threshold
Result using a localised threshold
Original Image
Result using a global threshold
Result using a localised threshold
Original Image A Result of Image A
Original Image B Result of Image B
Original Image - Horizontal Crack
Result of the original image - Horizontal Crack
Original Image
Result of the Original Image
Original Image
Result of the Original Image
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)
An pavement image containing no cracks
Result of the Original Image
An pavement image containing no cracks
Result of the Original Image
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
Any Question?