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?