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