NOISE REMOVAL IN CRACK DETECTION ALGORITHM

ON ASPHALT SURFACE IMAGES

Siwaporn Sorncharean and Suebskul Phiphobmongkol

Department of Computer Engineering, Chulalongkorn University, Pathumwan, Bangkok, Thailand

Keywords: Image Processing, Crack Detection, Noise Removal, Asphalt Surface.

Abstract: This paper presents an image processing technique for noise removal in the intermediate stage of crack

detection algorithm. Unlike noise in other domains, noise in this kind of image is unique in terms of size and

dispersal. This technique is based on Newton’s theory of universal gravitation. The technique highlights

noise within an image by giving low values to noise objects while giving high values to cracks, thus,

making it simple to indicate an object as a noise or a crack. This method gave good results in removing

noise from crack segmentation algorithm.

1 INTRODUCTION

Highway management system is typically used for

estimating the budget and for making maintenance

plan. Like all systems, the input of correct data is

essential. Submitting incorrect raw data can envisage

circumstances that would cause grave financial

distress to local, regional, and national governments.

When looking at the area of pavement distress,

visual inspection by human inspectors is time

consuming, requires too many professional

inspectors, and is financially restrictive. Moreover,

distress classifications and measurement are

subjective. Two inspectors may give different results

of distress information even if they are looking at the

same thing.

To solve these problems, automatic crack

monitoring systems were applied. An automatic

system (Pynn, 1999) can be separated into two

phases. In the first phase, the system collected road

surface images using a camera installed on a survey

vehicle. In the second phase, an automatic

processing of collected images was performed to

locate and measure distress.

A major problem of this automatic system was

the accuracy of distress information from automatic

processing of collected images. Many researches

were done to solve this problem by using image

processing techniques. Most crack detection

algorithm consisted of two parts, segmenting crack

lines and identifying them. For example, edge

detectoin algorthim (Yu, 2007), wavelet transform

technique (Subirats, 2006) and grid cell analysis

(Xu, 2006) (Sorncharean, 2008) were used to find

crack lines, and artificial intelligence techniques

(Zhang, 2004) (Tomikawa, 1999) (Meignen, 1997)

were used to classify cracked area.

Since the segmentation phase output still

contained noise, as a result, cracked areas were

misclassified and accuracy of crack detection

algorithm was reduced. To solve the problem, this

paper proposed a technique to remove noise in the

intermediate stage of crack detection algorithm. This

technique is based on Newton’s theory of universal

gravitation.

2 CRACK DISTRESS ON

ASPHALT SURFACE IMAGES

Figure 1: Example of crack on asphalt surface images

(transportation information center, 2002).

Crack is one of the major categories of common

asphalt pavement surface distress. Crack may result

from weathering, aging, or structural caused by

repeated traffic loadings. Most inspectors who

evaluate pavement surface conditions identify

269

Sorncharean S. and Phiphobmongkol S. (2009).

NOISE REMOVAL IN CRACK DETECTION ALGORITHM ON ASPHALT SURFACE IMAGES .

In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, pages 269-272

DOI: 10.5220/0001797902690272

 SciTePress

different types of crack, and link them to causes and

appropriate maintenance. There are six types of

crack, i.e. transverse, reflection, slippage,

longitudinal, block, and alligator crack.

(Transportation Information Center, 2002)

When capturing pavement images, crack line

appeared in an image as long strip of pixels which

perceptibly darker than background, as shown in 2.

Crack segmentation phase in the crack detection

algorithm try to extract crack lines using the crack

feature, darker lines on the background.

Unfortunately, some dark strips or spots are also

sorted out. It is hard to distinguish crack lines and

dark strips and to identify crack type with

confounding objects.

3 PRIOR WORK

The prior work (Sorncharean, 2008) of this research

involved a pavement survey system using area scan

cameras. Each camera had a resolution of 1024 x

960 pixels. A camera covered approximately 1.86 x

1.75 square meters with ground resolution of about

1.8 mm/pixel. The image processing was run on an

Intel Centrino Duo 2.16 GHz computer with 1GB

RAM.

The prior work focused on crack segmentation

phase with enhanced grid cell analysis. The results

of the work, as shown in Figure 2, showed that the

segmentation phase could extract cracks from the

sample images but there were still some noise in the

result images which could confound crack

identification algorithm in the later step.

To handle this problem, noise removal phase

was proposed and applied between the crack

segmentation phase and crack identification phase.

This phase helps removing noise in the intermediate

result images which are the input of the crack

identification phase.

(a)

(b)

Original Result

Figure 2: Examples of noise in result images.

4 THE PROPOSED APPROACH

Noise in the intermediate result is a problem for

classifying crack type. Unlike white noise or salt and

pepper noise (Gonzalez, 1992), this type of noise

cannot be removed by using filters. Noise is a small

object that looks like small piece of crack, thus,

Figure 2: Example of Crack on Asphalt Surface Images

(Transportation Information Center, 2002).

Crack and non-crack objects are blended

together. As a result, it is difficult to identify the

cracking area.

Figure 2 shows the result images with noise

objects from the prior work. Noise could be caused

by dark spots on the original image, as shown in

Figure 2 (a). Another cause of noise is other types of

pavement distress. For example, Figure 2 (b) shows

an original image with a patched area which causes

the result image containing too much noise.

4.1 Crack Appearance

From segmentation process, an object is mostly

justified to be a crack if it has a huge area (pixel

counts), but this is not always true. For example,

large objects (a), (e), and (g) in 0(a) are parts of

crack lines, but many small objects in 0(a) are also

parts of crack lines too.

In contrast to 0(a), 0(b) shows noise objects on

non-cracking area. However, these objects look like

small objects in 0(a), e.g. object (b), (c), and (h). The

distinction between the small objects in 0(a) and

0(b) is the dispersal of the objects themselves. Small

objects in 0(a) are close to huge objects, while the

objects in 0(b) spread over the whole region. From

this distinction, the technique for telling the

difference between crack and non-crack objects was

proposed with an assumption that a crack object is

an object which has large area or stays close to a

large object.

(a) (b)

Figure 3: Enlarged Elements.

4.2 Crack Gravitation

Modern physics describes gravitation as a natural

phenomenon that objects attract each other with a

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270

force of gravitational attraction. The Newton's

theory of universal gravitation states that the force is

directly dependent upon the masses of both objects

and inversely proportional to the square of the

distance between their centers (Drakos, 1999).

Like force of gravitational attraction, a crack

object is considered as part of a crack line or not by

its area and the distance between it and other

objects. For the purpose of noise removal,

gravitation feature is applied to calculate

gravitational force between each pair of objects. If

the force is strong enough, it indicates that the object

is close to a large object and is considered a crack.

4.3 Gravitation Feature

If an object A has a pixel area of

a and an object B

has a pixel area of

a , then the magnitude of

gravitational force feature

on object A will be

directed toward object B as shown below,

(1)

where

r is the shortest distance among the distance

between the two tips of object A and B.

Since the gravitational force is directly

proportional to the product of pixel areas of the two

interacting objects, larger objects will attract each

other with a greater gravitational force. In contrast to

the area, the force is inversely proportional to the

square of the shortest distance,

r , as described

above. Farther distance will result in weaker

gravitational forces.

Due to the fact that most crack objects are

narrow and almost aligned, the center of gravitation

is then applied to the tips of the objects in order to

increase gravitational force to the surrounding

objects. With this concept, the gravitational force

Figure 4: Objects on a Crack Line.

Table 1: Object information for Figure 4.

Interacting

Object Number

Area

(pixel)

Distance

(pixel)

Gravitational

Force

(Feature Value)

(1) 682.00 13.04 1,207.54

(2) 489.00 31.58 147.63

(3) 1,444.00 160.59 16.85

Figure 5: Crack gravitation.

abruptly changes with the distance, thus, make it

easier to perceive an object as a crack.

Figure 5 shows an example of applying

gravitational force feature to the crack objects.

Crack objects are displayed in white object on the

black background. The gravitational force feature

applied to the tip of the objects. The feature values

are shown as grey level. The brighter of the pixel,

the higher value of the feature. The feature shows

that bigger crack object gives stronger gravitational

force value, as can be seen in 0 where strong force

resulted from big objects can reach farther objects.

Moreover, closer crack objects tend to present

higher value of the feature.

In order to classify crack, the area and the

gravitational force are considered. Large area objects

or strong gravitational forces are signs of crack

objects. Otherwise, the objects are indicated as

noise. In other words, weak gravitational forces

show a characteristic of random orientation of small

objects.

5 EXPERIMENTAL RESULT

To test the capability of this feature, the concept was

applied to every pair of crack objects in an example

image to show the feature value. 0 shows an

example of noisy image. Considering object (0) in

Figure 6: Objects on Non-Crack Line.

Table 2: Object information for Figure 6.

Interacting

Object Number

Area

(pixel)

Distance

(pixel)

Gravitational

Force

(

Feature Value)

(1) 1,444.00 141.17 4.46

(2) 32.50 37.01 1.46

(3) 16.00 30.87 1.03

NOISE REMOVAL IN CRACK DETECTION ALGORITHM

ON ASPHALT SURFACE IMAGES

271

Figure 4, it is on a crack line with an area of 301

pixels. The other significant object information is

shown in Table 1 with their feature values arranged

in descending order. The object (1) is on the same

crack line as the considered object (0). Moreover,

the object (1) is the closest object to the object (0).

Unlike object (1), the object (2) is smaller and

farther than the object (1). As a result, object (1)

gives a value of 1,207.54 which is the highest value

of the gravitational force towards object (0) while

the object (2) gives a value of 147.63 which is a

much smaller value.

Looking at object (3) in the 04, it is the biggest

object but very far away from the considered object

(0). Consequently, it gives a value of 16.85 which is

a small amount of feature value.

0 shows the object where the considered object

(0) is noise object with an area of 61 pixels. Partial

object information is shown in Table 2. Since the

considered object (0) is small, the biggest object

gives a little feature value, 4.46, compared to feature

value of the object (1), which is 1,207.54, in 0.

Due to a high range of the feature value, it has

the ability to distinguish an object as a noise object

or a crack. This concept was applied to the example

images in 0. 0 column (a) shows the original

pavement surface with the crack lines. 0 column (b)

shows the result image with too many noise objects

from crack detection algorithm. After applying this

feature for removing noise, the crack lines appear

obviously. The results are shown in 0 column (c).

6 CONCLUSIONS

This paper introduces an image processing feature

for noise removal in the intermediate result images

of the crack detection algorithm. Unlike normal

noise, noise of this kind of image is unique in terms

of size and dispersal. This feature of noise removal

is based on the theory of universal gravitation. This

theory is applied to the objects for keeping crack

objects separated form noise.

(a) (b) (c)

Figure 7: Result images.

Applying this feature to noisy images, the crack

lines are easier to be notice and classified in the

identification phase. With less noise, the

identification algorithm gives more accurate output

for highway management system. In the big picture,

the proposed method helped improve the accuracy of

the crack detection algorithm (Sorncharean, 2008)

and providing more reliable information to the

highway management system.

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Document URL: http://theory.uwinnipeg.ca/

mod_tech/node54.html.

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