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7/27/2019 detection of pavement distress using laser technology.pdf
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DETECTIO
Submit
for the
M2
DEPA
OF PAVEMENT DISTR
LASER TECHNOLOGY
SEMINAR REPORT
ted in partial fulfillment of the requirem
award of M.Tech Degree in Civil Enginee
of University of Kerala
Submitted by
BHAGEERATHY K P
Traffic and Transportation Engineerin
Roll No: 122607
TMENT OF CIVIL ENGINEE
COLLEGE OF ENGINEERING
TRIVANDRUM
2013
SS USING
ents
ring
RING
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DEPA
This is to cePAVEMENT DIST
of the work done by
fulfillment of the requ
(Traffic and Transpor
year 2013.
Guided by
Dr. Manju V S
Associate Professor
Department of Civil E
College of EngineeriTrivandrum
TMENT OF CIVIL ENGINEE
COLLEGE OF ENGINEERING
TRIVANDRUM
2013
CERTIFICATE
rtify that this seminarreport entitled
ESS USING LASER TECHNOLOGY i
Bhageerathy K P under my guidance t
irements for the award of M.Tech Degree i
tation Engineering) under the University o
Profe
Dr.
ngg. Depart
ng Colle
RING
ETECTION OF
a bonafide record
wards the partial
Civil Engineering
Kerala during the
sor (PG Studies)
Satyakumar
Professor
ent of Civil Engg.
ge of EngineeringTrivandrum
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ACKNOWLEDGEMENT
I am sincerely indebted to my guide Dr. Manju V. S., Associate Professor,
Department of Civil Engineering, College of Engineering Trivandrum, for her
valuable guidance and suggestions in preparing this seminar report.
I would also like to thank Prof. Jyothis Thomas, Professor and Head,
Department of Civil Engineering, Dr. M. Satyakumar, Professor (P.G Studies),
Prof. Jayaprakash Jain, Staff Advisor and Prof. Leema Peter, Assistant Professor
(Project coordinator), Department of Civil Engineering, for their encouragement and
support.
I would also like to express my sincere gratitude to all my friends who
supported and helped me in completing this report.
BHAGEERATHY K. P.
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ABSTRACT
There are several reasons which contribute towards pavement distresses.
Detection and repair of distresses on time is very much necessary for preventing the
failure of pavements. Usual method of detection of distresses using human
observations is extremely tedious and prone to errors. To overcome the limitations of
visual evaluation, several attempts have been made to automate the process of
detection. One such technology is by using laser scanners. They can be used
effectively for getting 3D pavement surface data.
Two case studies were dealt with for understanding the application of laser
technology in distress detection. The first study deals with the detection of potholes
and severity classification using laser technology. The second study evaluates the
feasibility of using laser technology to detect cracks. Both studies show that laser
technology is very promising for pavement distress detection.
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CONTENTS
Page No.
1. INTRODUCTION 1
1.1 General 1
1.2 Objectives 2
2. PAVEMENT DISTRESSES 2
2.1 General 2
2.2 Types of pavement distresses 2
2.2.1 Surface defects 2
2.2.2 Cracks 3
2.2.3 Deformation 6
2.2.4 Disintegration 8
3. LASER TECHNOLOGY 9
3.1 General 9
3.2 Basic concept 9
3.3 Principle of 3D laser scanners 10
4. CASE STUDY 1 12
4.1 General 12
4.2 Need for the study 12
4.3 Methodology 12
4.4 Image processing techniques for detecting potholes
using laser pattern 13
4.4.1 Multi-window median filtering 13
4.4.2 Tile partitioning 14
4.4.3 Laser line deformation detection approach 15
4.5 Pothole severity classification 16
4.6 Experimental results 18
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4.7 Concluding remarks 19
5. CASE STUDY 2 20
5.1 General 20
5.2 System set-up 20
5.3 Experimental tests 22
5.3.1 Controlled test procedure 22
5.3.2 Field test procedures 25
5.4 Findings and concluding remarks 27
6. CONCLUSIONS 28
REFERENCES 29
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LIST OF FIGURES
Figure No. Title Page No.
Fig 2.1 Fatty surface 3
Fig 2.2 Streaking 3
Fig 2.3 Alligator crack 4
Fig 2.4 Longitudinal crack 4
Fig 2.5 Edge crack 5
Fig 2.6 Shrinkage crack 5
Fig 2.7 Slippage 6
Fig 2.8 Rutting 6
Fig 2.9 Corrugation 7
Fig 2.10 Potholes 9
Fig 3.1 Illustration of optical triangulation principle 10
Fig 3.2 Principle of 3D laser scanners 11
Fig 4.1 Deformed laser pattern on detecting a pothole 13
Fig 4.2 Four masks used for filtering 13
Fig 4.3 Image thresholding 14
Fig 4.4 Template laser line 15
Fig 4.5 Template matching method for pothole shape
estimation 16
Fig 4.6 Architecture of the neural network 18
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Fig 4.7 Results extracted from image database 19
Fig 5.1 Sensing vehicle integrated at the Georgia
Institute of Technology 21
Fig 5.2 Laser crack measurement system and
projection of laser 21
Fig 5.3 Visualization of 3D pavement surface data 22
Fig 5.4 A gap between two solid wood boards to simulate
a crack with known width 23
Fig 5.5 Two lighting conditions 24
Fig 5.6 Crack segmentation results on simulated cracks 24
Fig 5.7 Test results on a crack with low intensity contrast 26
LIST OF TABLES
Table No. Title Page No.
Table 4.1 Distress classification guideline 18
Table 4.2 Severity level comparison 19
Table 5.1 Scores for the controlled tests 25
Table 5.2 Scores for the second field test 27
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1
1. INTRODUCTION
1.1 General
Quantification of pavement distresses like cracks, potholes etc. is one of the
most important tasks in determining optimal strategies of pavement maintenance.
These distresses, which are caused by several reasons, curtail the service life of
pavements. Once initiated, distress increases in severity and extent, allowing water to
ingress the pavement. Over the past years, a significant amount of efforts has been
spent on developing methods to objectively evaluate the condition of pavements. For
the inspection of the surface distress of highway pavements, the most widely used
method to conduct such surveys is based on human observation. This approach is
extremely labour-intensive, prone to errors, and poses hazards. To overcome the
limitations of the subjective visual evaluation process, several attempts have been
made to develop automatic procedures. Most systems use optic images and vision
technology to automate the process. However, due to the irregularities of pavement
surfaces, there has been a limited success in accurately detecting distresses and
classifying distress types. In addition, most systems require complex algorithm withhigh levels of computing power.
With the advancement in sensor technology, an advanced 3D laser system has
become available. The 3D laser scanning is one of the exceptionally versatile and
efficient technologies for accurately capturing large sets of 3D coordinates. 3D laser
scanner uses a technique that employs reflected laser pulses to create accurate digital
models of existing objects. For 3D survey, detection of pavement distresses, such as
potholes or patches, is possible application where laser scanner technology excels.
The advancement of the scanner has invoked many applications such as civil
engineering, natural hazard investigations, heritage, landscape design, and tunnel and
cave survey, and pipelines. The most popular applications include archeology, as-built
surveying, re-modeling of tunnels, bridges, and other civil structures, topographic
mapping for base maps, engineering design, and mining fields, and reconstruction of
traffic accidents, and urban planning.
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1.2 Objectives
The objectives of the study are:
i. To know about various distresses occurring on pavements.
ii. To understand the concept of laser technology.
iii. To study about the applications of laser technology in detection of distresses in
pavement.
2. PAVEMENT DISTRESSES
2.1 General
This chapter describes the various distresses that occur in pavements.
2.2 Types of Pavement Distresses
The various types of pavement distresses are grouped as:
Surface defects: which include fatty surfaces, smooth surfaces, streaking and
hungry surfaces
Cracks: under which hair line cracks, alligator cracks, longitudinal cracks, edge
cracks, shrinkage cracks, and reflection cracks comes
Deformation: which includes grouped slippage, rutting, corrugations, shoving,
shallow depressions, and settlements and upheavals
Disintegration: covering slipping, loss of aggregates, raveling, potholes and edge
breaking
2.2.1 Surface defects
These are associated with the surfacing layers and may be due to excessive or
deficient quantities of bitumen in these layers.
Fatty surface:
Fatty surface shown in Fig 2.1 results when the bituminous binder moves
upward in the surfacing and collects as a film on the surface. The causes for a fatty
surface are excessive binder, loss of cover aggregates in surface dressing, non uniform
spreading of cover aggregates, too heavy prime or tack coat and excessive heavy axle
load.
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Fig 2.1 Fatty surface (Source: Google image)
Smooth surface:
A smooth surface has a very low skid resistance and becomes very slippery
when wet. A primary cause for the smooth surface is the polishing of aggregates
under traffic. Also excessive binder can result in a smooth surface.
Streaking:
Streaking is characterized by the appearance of alternate lean and heavy lines
of bitumen either in longitudinal or transverse direction. It is shown in Fig 2.2.
Fig 2.2 Streaking (Source: Google image)
Hungry surface:
Hungry surface is characterized by the loss of aggregates from the surface or
the appearance of fine cracks. One of the reasons for hungry surface is the use of less
bitumen in the surfacing. Sometimes this condition may also appear due to use of
absorptive aggregates in the surfacing.
2.2.2 Cracks
Formation of cracks is a common defect in bituminous surfaces. The crack
pattern can, in many cases, indicate the cause of the defect. The common types of
cracks are:
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Hair-line crack:
These appear as short and fine cracks at close intervals on the surface. These
cracks are caused by insufficient bitumen content, excessive filler at the surface and
improper compaction.
Alligator crack:
These appear as interconnected cracks forming a series of small blocks which
resemble the skin of an alligator as shown in the Fig 2.3. Main reasons for alligator
cracks are excessive deflection of the surface over unstable subgrade, excessive
overload by heavy vehicles, inadequate pavement thickness ageing of binder etc.
Fig 2.3 Alligator crack (Source: Google image)
Longitudinal crack:
These cracks appear more or less, on a straight line along the road. The
reasons may be due to alternate wetting and drying beneath the shoulder owing to
poor drainage or due to depressions in the pavement edge. It is shown in Fig 2.4.
Fig 2.4 Longitudinal crack (Source: Google image)
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Edge crack:
They are found parallel to the outer edge of the pavement as shown in Fig 2.5.
They are caused by lack of lateral support from the shoulder, settlement of underlying
material inadequate surface drainage, shrinkage, frost heave etc.
Fig 2.5 Edge crack (Source: Google image)
Shrinkage crack:
These are cracks appearing in the transverse direction, or as interconnected
cracks forming a series of large blocks. The pavement itself appears to have no
deterioration or deformation, but it is the top surfacing that seems to have become old
and cracked. The primary cause for such cracks is the shrinkage of the bituminous
layer itself with the age. It is shown in Fig 2.6.
Fig 2.6 Shrinkage crack (Source: Google image)
Reflection crack:
They are the sympathetic cracks that appear in the bituminous surfacing over
joints and cracks in the pavement underneath. The pattern may be longitudinal,
transverse, diagonal or block. They occur most frequently on overlays on cement
concrete pavements or on cement-soil bases. They may also occur in overlays on
flexible pavements where cracks in the old pavement have not been properly repaired.
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2.2.3 Deformation
Any change in the shape of the pavement from its original shape is a deformation.
Slippage:
It is the relative movement between the surface layer and the layer beneath. It
is characterized by the formation of crescent-shaped cracks as shown in Fig 2.7, that
point in the direction of the thrust of the wheels on the pavement surface. It is caused
by unusual thrust of wheels in a particular direction, inadequacy of tack coat or prime
coat, lack of bond between the surface and the lower course due to excessive
deflection etc.
Fig 2.7 Slippage (Source: Google image)
Rutting:
It is a longitudinal depression or groove in the wheel tracks as shown in Fig
2.8. Ruts are usually of the width of wheel path. If rutting is accompanied by adjacent
bulging, it may be the sign of subgrade movement or weak pavement. The causes of
rutting are heavy channelized traffic, inadequate compaction of the mix, improper mix
design, weak pavement, intrusion of subgrade clay into base layer etc.
Fig 2.8 Rutting (Source: Google image)
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Corrugation:
It is the formation of fairly regular undulations across the bituminous surface
as shown in Fig 2.9. It cause discomfort to motorists. Its causes are lack of stability in
the mix, excessive binder, high proportion of fines, round and smooth aggregates, soft
binder and faulty laying of surface course.
Fig 2.9 Corrugation (Source: Google image)
Shoving:
It is a form of plastic movement within the layer resulting in localized bulging
of the pavement surface. Shoving occurs characteristically at points where traffic
starts and stops such as intersections, bus-stops etc. The first indication of shoving is
the formation of slippage cracks which are crescent shaped cracks. Shoving can becaused by lack of stability in the mix, lack of bond between bituminous surface and
underlying layer, heavy traffic movement of a start and stop type, use of non-volatile
oil on roller wheels etc.
Shallow depression:
They are localized low areas of limited size, dipping about 25mm or more
below the desired profile, where water will normally collect. If not rectified in time,
they may lead to further deterioration of the surface and cause discomfort to traffic.
They are caused by the settlement of lower pavement layers.
Settlement and upheaval:
They are characterized by large deformations of the pavement. They are
extremely uncomfortable to traffic and cause serious reduction in speed. The causes
are inadequate compaction of the fill at locations behind bridge abutments, excessive
moisture in the subgrade, inadequate pavement thickness and frost heave conditions.
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2.2.4 Disintegration
There are some defects which if not rectified immediately, result in the disintegration
of the pavement into small, loose fragments. Disintegration, if not arrested in the early
stages, may necessitate complete rebuilding of the pavement.
Stripping:
It is characterized by the separation of bitumen adhering to the surfaces of the
aggregate particles in the presence of moisture. This may lead to loss of bond and
subsequently to loss of strength and materials from the surface. The reasons for
stripping are use of hydrophilic aggregates, inadequate mix composition, continuous
contact of water with the coated aggregate, over heating of aggregate or binder,
presence of dust or moisture on aggregate when it comes in contact with the bitumen,
occurrence of rain or dust storm immediately after construction, opening the road to
fast traffic before the binder has set, use of improper grade of bitumen, ageing of
bitumen etc.
Loss of aggregate:
It occurs in surfaces which have been provided with surface dressing. The
surface presents a rough appearance, with some portions having aggregates intact andothers where aggregates have been lost. The loss of aggregates can occur due to
ageing and hardening of binder, stripping of binder from aggregates due to wet
weather, wet or dusty aggregate, insufficient binder, aggregate having no affinity to
the binder, insufficient rolling before opening to traffic, cold-spraying of bitumen or
delaying the spreading of aggregates over sprayed bitumen.
Ravelling:
Ravelling is generally associated with premixed bituminous layers. It is
characterized by the progressive disintegration of the surface due to the failure of the
binder to hold the materials together. The raveling process generally starts from the
downwards or from edge inward. It usually begins with the blowing off of the fine
aggregates leaving behind pock marks on the surface. The reasons for raveling are
inadequate compaction, construction during wet or cold weather, use of inferior
quality of aggregates, insufficient binder in the mix, ageing of binder, poor
compatibility of binder and aggregate, over heating of mix and improper coating of
aggregates by the binder.
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Pot-hole:
Pot-holes are bowl-shaped holes of varying sizes as shown in Fig 2.10 in a
surface layer or extending into the base course caused by localized disintegration of
material. They usually appear after rain. The reasons for formation of potholes are
ingress of water into the pavement through the surfacing course, lack of proper bond
between the bituminous surfacing and base, insufficient bitumen content, too thin a
bituminous surface which is unable to withstand the heavy traffic, too much or too
few fines.
Fig 2.10 Potholes (Source: Google image)
Edge-breaking:
The edge of the bituminous surface gets broken in an irregular way, and if not
remedied in time, the surfacing may peel off in large chunks at the edges. The causes
of edge breaking are infiltration of water, worn out shoulders resulting in insufficient
side support to the pavement, inadequate strength at the edge of the pavement due to
inadequate compaction etc.
3. LASER TECHNOLOGY
3.1 General
This section describes the laser technology that can be used for collecting 3D
pavement profile data for detecting the distresses in pavement.
3.2 Basic Concept
Many 3D data acquisition systems are on the basis of the triangulation
principle, as shown in Fig 3.1. In such systems, a specific and often fixed pattern of
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illumination (i.e., structured illumination) is projected onto an object to be measured.
The structured light that is projected is a laser line for the system proposed. A digital
area scan camera with a charge-coupled device (CCD) or complementary metal oxide
semiconductor (CMOS) sensor is placed at a known distance and an oblique angle ()
with respect to the light projector. The camera takes images of the structured light.
Then the deformations of the laser line on the object are analyzed to evaluate the
depth (z-axis) for each point with a known horizontal position (x-axis) on the object.
The 3D system is usually coupled with an encoder, which enables the system to
obtain the y-axis position (i.e., the driving direction). Consequently, a complete three
dimensional set of points of the objects surface can be acquired. In addition, such 3D
systems can provide both depth and intensity information. The intersection between
the emitted structured light and the field of view of the digital camera defines the
measurement range of a 3D system.
Fig 3.1 Illustration of optical triangulation principle
(Source: Tsai and Li, 2012)
3.3 Principle of 3D Laser Scanners
A 3D laser scanner, also known as LiDAR (Light Detection And Ranging),
can be considered as an auto-scanning total station which is able to acquire thousands
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of points in a few seconds. The laser scanner is operating to obtain point coordinates
referenced to an internal coordinate system as shown in Fig 3.2. On basis of the mode
of platform, LiDAR can be ground-based, air-borne, or space-borne. Usually, the
effective distances between the scanner and the objects are in a short-range (< 1 m),
mid-range (1-30 m), long-range (30 m-1 km), or super-range for air-borne system
(600 m-3000 m). One of the advantages of 3D laser scanner is to measure 3D
coordinates of a complicated object in a distance which may hinder further physical
contact of the object.
Fig 3.2 Principle of 3D laser scanners
(Source: Chang et al., 2011)
Ground-based LiDAR is composed of a high pulse rate laser ranger and a
directional mirror, thus to accurately measure the range from laser head to the target
and then computed along with the mirror angles to obtain the 3D coordinates of the
target. Typical 3D scanner system is composed of three parts of components:
1. Laser ranger: including transmitter, receiver, detector, amplifier, timing counter
and other electronics. To assure that laser light pulse is transmitted and received in a
defined field of view, the light is transmitted and received in an identical path.
2. Optical or mechanical scanning components: for guiding the light in a specified
direction, e.g. rotation mirror, plan rotating mirror.
3. Control and data processing components: including computer, and softwares for
scaning control, preprocessing and post processing such as data processing and
analysis.
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4. CASE STUDY 1
DETECTION OF POTHOLES USING LASER
TECHNOLOGY
4.1 General
This case study done by Yu and Salari (2011) deals with the detection of potholes and
its severity measurement using laser imaging.
4.2 Need for the Study
Over the years, Automated Image Analysis Systems (AIAS) have been
developed for pavement surface analysis and management. The cameras used by most
of the AIAS are based on Charge Coupled Device (CCD) image sensors where a
visible ray is projected. However, the quality of the images captured by the CCD
cameras was limited by the inconsistent illumination and shadows caused by sunlight.
To enhance the CCD image quality, a high-power artificial lighting system can be
used, which requires a complicated lighting system and a significant power source.
4.3 Methodology
The proposed laser-based optical system consisted of an active light
source that projects a line pattern of laser beams onto the pavement surface, a camera
for capturing images, and the image processing algorithms that identify the potholes,
as shown in Fig 4.1. After the pavement images were captured, regions corresponding
to potholes were represented by a matrix of square tiles and the estimated shape of
pothole is determined. Following the pothole detection, a feed-forward neural network
is used to determine its severity. The vertical, horizontal distress measures, the total
number of distress tiles and the depth index information are calculated providing input
to a three-layer feed-forward neural network for pothole severity. To validate the
system, actual pavement pictures were taken from pavements both in highway and
local roads, and experiments were done.
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Fig 4.1
4.4 Image Processing
The main
regions in the image.
image is searched fo
different image proces
4.4.1 Multi-window
The removal of impuvideo sequences are c
laser line. The laser
undesired external lig
to perform noise redu
known nonlinear filte
region of an image. Si
liner structures in th
directional median val
Fig 4.2 Four
13
Deformed laser pattern on detecting a pot
(Source: Yu and Salari, 2011)
Techniques for Detecting Potholes Using
aim of the image processing module is to
fter extracting the laser line from the backg
any deformation in the shape of the lase
sing techniques for detecting potholes using
edian filtering
lse noise is an important issue in potholeaptured as image frames, the frames are sc
line is affected by the superposition of a
ting. A multi-window median filter is appli
tion in an image. The standard median (M
r that eliminates the noise and performs
nce the detection of pavement distress invol
pavement image, a multi-stage median f
ues as represented by masks in Fig 4.2, is co
asks used for filtering (Source: Yu and S
hole
aser Pattern
extract laser color
round, the resulting
r line pattern. The
a laser pattern are:
etection. After theanned to detect the
certain amount of
d in the initial step
D) filter is a well-
ell in the smooth
es the detection of
ilter which uses 4
sidered.
lari, 2011)
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The multi-stage median filter could be used to reduce the noise while preserving
much of the detail in the 2-D image and produces comparable results with the
standard median filter.
4.4.2 Tile partitioning
Thresholding:
Image tiling starts with binarizing the image using a thresholding operation.
Thresholding is a widely used technique for image segmentation and feature
extraction. In many applications of image processing, the gray levels of pixels
belonging to the object are substantially different from the pixels belonging to the
background. During the thresholding process, individual pixels in an image are
marked as object pixels if their value is greater than some threshold value and as
background pixels if lower. In this study, a laser line pixel is given a value of 0
while a background pixel is given a value of 1. Finally, a binary output image is
created, as shown in Fig 4.3.
Fig 4.3 Image thresholding (Source: Yu and Salari, 2011)
Noise removal:
In this step, morphological closing is applied in order to fill small holes,
bridge the thin gaps in the binary image, connect nearby laser line pixels without
changing the laser line area significantly, and smooth the boundaries. The noise in the
binary image is reduced by labeling connected components and counting the number
of connected pixels. The operation scans the image and groups them together into
components based on pixel connectivity, i.e. all pixels in connected component share
similar pixel intensity values and are connected with each other. Based on the number
of pixels in a connected component, any connected components less than a pre-
defined value would be considered as noise and removed from the image.
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Tile partitioning:
The method p
than pixels. The outp
called tiles. Each
pavement surface. T
complexity relative t
background noise bec
be classified as a poth
each tile is classified
classify a tile as a las
mean value of each tivalue is considered as
labeled with 0. In th
4.4.3 Laser line defor
The laser line
deformed pattern. Fo
produces a pattern wit
The deformation of th
detect the deformatio
predefined laser line t
then compared with th
Fig 4.4
15
roposed in this study relies on sub-images
t image from the previous step is divided i
ile is 40x40 pixels which covers a 2x2
e tile-based method significantly reduces
pixel-based computations. As a result, it
use a few noise pixels alone would not be s
ole tile. After the binary image is sub-divid
as either a laser line tile or non-laser line ti
er line tile is based on the global mean val
le. Any tile that has a mean value lower tha laser line tile and would be labeled 1; ot
is way, a tile-based matrix is generated.
mation detection approach
in the pothole area of the image produces a
r example, the projection of a laser line
h a different shape than the projection of a la
laser pattern can reveal the presence of the
n of the laser line, a template matching
emplate is generated as shown in Fig 4.4. T
e predefined template frame to detect the def
emplate laser line (Source: Yu and Salari
of pavement rather
to 625 sub-images
inch block on the
the computational
is less affected by
fficient for a tile to
d into square tiles,
le. The decision to
ue versus the local
n the global meanerwise it would be
isible contour of a
onto a plane area
ser line onto a ball.
pothole. In order to
ethod is used. A
e input frames are
ormation.
, 2011)
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16
Template matching method:
Pothole shape estimation:
After the tile partitioning step, each frame is compared with the template
frame, tile by tile, for any deformation. The number of tiles in each row that differ
between the input frame and the template frame are calculated. If the row that has the
maximum deformation of1s is above the row that has the maximum deformation of
0s, the laser line would be intersecting an obstacle in the scene. Otherwise, the row is
determined to be an actual deformed tile row due to a pothole. The row is stored in a
new matrix as the first row. This matching process will continue until no further
deformation is detected. All rows that qualify for deformation are stored in the new
matrix. The output matrix would be an estimated shape of the pothole. The process is
shown in Fig 4.5.
Fig 4.5 Template matching method for pothole shape estimation
(Source: Yu and Salari, 2011)
Depth index:
Depth information is defined based on the extent of the deformation that
affects rows in each frame. For example, in Fig 4.5 (a), deformation could be detected
in 2 rows, so the depth information of this frame would be 2. All depth information is
stored frame by frame until no further deformation is detected. The average of this
information is computed as the depth index for the detected pothole and stored for
further analysis.
4.5 Pothole Severity Classification
Distress extracted through the process as explained in the previous section can
be classified into different types of potholes with various severity levels (low,
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moderate, or high). In
distress classification.
the differences betwe
the distress type by fi
Histograms are used t
distress tiles (zeros) i
measure is determine
deformed tiles in adja
Where VD is the vert
the number of colum
computed by accumul
for adjacent image ro
Where HD is the hori
the number of rows, re
If both horizontal and
classified as a pothol
parameters (the vertic
number of distress tile
forward neural networ
input nodes, 8 hidden
The severity level of
4.1.
17
the study, a three-layer feed-forward neural
The distress measure in an image is calculat
n adjacent histogram values. The neural ne
nding the unique pattern of uniformity in th
measure the statistical information by cou
each column, row and the whole matrix.
d by accumulating the differences betwe
ent columns using equation,
ical distress measure, Hv is the vertical his
ns, respectively. Similarly, the horizontal
ating the differences between the number
s using equation,
ontal distress measure, h is the horizontal h
spectively.
vertical distress measures are having large v
e. A neural network is used for pothole
al distress measure, the horizontal distress
s and the depth index) are used to provide t
k. The architecture of the neural network w
nodes and 5 output nodes is shown in Fig 4.6
the pothole is classified according to the d
network is used for
d by accumulating
work distinguishes
ese distress values.
ting the number of
he vertical distress
n the numbers of
(4.1)
togram, and Nc is
istress measure is
f deformation tiles
(4.2)
istogram, and Nr is
alue, the distress is
lassification. Four
measure, the total
he inputs to a feed-
ich has a total of 4
.
ta shown in Table
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Table 4.1 Distre
Distress
type
Vertic
me
Low
Moderate
High
4.6 Experimental Re
The proposed algorith
images (10 images f
sample distress classif
Fig 4.7 (a) and (b) sho
and (e) shows the poth
18
Fig 4.6 Architecture of the neural networ
(Source: Yu and Salari, 2011)
ss classification guideline (Source: Yu and
l distress
asure
Horizontal distress
measure
Tota
distr
5 > 5
5 > 5 4
5 > 5 >
ults
m was implemented in MATLAB R2008b o
r each distress) taken from the road surf
ication results extracted from the image data
ws two typical road surface scans of pothole
ole image represented by tiles.
Salari, 2011)
l No. of
ess tiles
Depth
index
40 1
-120 2
120 3
n a set of over 100
ce. Fig 4.7 shows
ase.
images. Fig 4.7 (d)
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19
Fig 4.7 Results extracted from image database
(Source: Yu and Salari, 2011)
Table 4.2 summarizes the rating results from manual and proposed laser-based
approaches. It can be clearly seen that, in all tested samples, the severity level and the
crack type detected by the proposed method is in agreement with the level obtained
from the manual method.
Table 4.2 Severity level comparison (Source: Yu and Salari, 2011)
Sample No. Distress typeSeverity Level or Crack Type
Manual Proposed
1 Pothole Moderate Moderate
2 Pothole Moderate Moderate
4.7 Concluding Remarks
In the study, a laser based pothole detection and classification method using
advanced image processing techniques was used. It has been shown that the proposed
system allows complete automation with the evaluation of pavement potholes. In
comparison with other existing 2-D pavement distress detection and classification
methods, the proposed method has a better ability to discriminate the dark areas that
are caused by lane marks, oil spills, or shadows. The experimental results indicated
that the proposed system provides reliable and accurate results from the tested
samples.
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5. CASE STUDY 2
DETECTION OF ASPHALT PAVEMENT CRACKS
USING 3D LASER TECHNOLOGY
5.1 General
This study conducted by Tsai and Li (2012) was sponsored by the U.S. Department of
Transportation (US DOT) Research Innovative Technology Administration (RITA)
program. It deals with the evaluation of feasibility of using emerging 3D laser
technology to detect cracks under different lighting and poor intensity contrast
conditions.
5.2 System Set up
A sensing vehicle as shown in Fig 5.1 was integrated at the Georgia Institute
of Technology for collecting 3D pavement surface data. First, the 3D system,
composed of two high-performance laser profiling units, was mounted on the vehicle.
The field of view of the two units covered a full lanes width. The acquired 3D laser
profile was designed to have a 15 clockwise tilt angle to the pavements transverse
direction, as shown in Fig 5.2. This was to ensure that 3D transverse profiles canintersect with transverse cracks. Each profiling unit consisted of a 3D laser profiler
that uses a high-powered laser line projector, a custom filter, and a camera as the
detector. The profiling unit uses a light stripe, which is created by a 7W multiple
emitter laser diode and line-generating optics. The light stripe is projected onto an
objects surface, and its image is captured by the area scan camera. From the captured
image, range measurements are extracted.
With a two-unit setup, the Laser Crack Measurement System (LCMS)produces 4,160 3D data points per profile (2080 pixels2 units) covering a 4m
pavement width. The resolution in x direction (transverse profile direction) is
approximately 1 mm (4 m/4096 points). The accuracy is 0.5 mm in z direction
(elevation). The highest resolution in y direction (longitudinal) depends on the
distance measurement instrument (DMI) and the accompanying encoder. In the
integrated sensing vehicle, an encoder with 1024 pulses per revolution was installed
to trigger the acquisition of 3D continuous transverse profiles. The interval between
two 3D transverse profiles can achieve 2.3mm by using the encoder. The system can
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collect transverse pr
pavement surface dat
visualization of 3D pa
Fig 5.1 Se
Fig 5.2 Lase
21
files at 4.6mm intervals at a speed of 1
was then be acquired for detecting cracks.
vement surface data and a closer look at a cr
sing vehicle integrated at the Georgia Ins
Technology (Source: Tsai and Li, 2012)
crack measurement system and projecti
(Source: Tsai and Li, 2012)
00 km/h. The 3D
Fig 5.3 shows the
ck line.
itute of
n of laser
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22
Fig 5.3 Visualization of 3D pavement surface data
(Source: Tsai and Li, 2012)
5.3 Experimental Tests
Utilizing the integrated sensing vehicle, experimental tests were conducted to
consistently and quantitatively evaluate the feasibility of using 3D laser technology to
detect pavement cracks under different lighting conditions and low contrast
conditions. Two series of tests were conducted. One was the controlled laboratory test
on simulated cracks with known crack widths and depths, and the other was the field
test on real roadways. In the controlled tests, the objective was to assess the capability
of the 3D laser technology to detect different widths of cracks under different lighting
conditions. Four crack widths (1, 2, 3 and 5mm) under two extreme lighting
conditions (daytime and nighttime) were tested in the Georgia Institute of
Technologys campus laboratory. The crack depth was approximately 19 mm.
5.3.1 Controlled test procedure
A controlled gap between two solid wood boards was used to simulate a
pavement crack on the road. The width of the gap was measured before and after the
test with a caliper, as shown in Fig 5.4. Afterwards the integrated sensing vehicle was
driven over the road section by an operator to automatically collect the 3D laser data
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of both wood boards by using the 3D sensor system. With the 3D laser data, the
dynamic optimization was employed to segment the simulated cracks. Meanwhile, the
ground truth was manually digitized and extracted from the 3D laser data. The tests
were conducted during the daytime and nighttime, as shown in Fig 5.5. Table 5.1
shows the test results. Fig 5.6 shows only part of the controlled laboratory test results.
It includes four subsets of figures. Each subset of figures shows the 3D raw data on
the left and the crack segmented image produced by using the dynamic optimization
algorithm on the right. Fig 5.6(a) and 5.6(b) show the data collected for a 1mm wide
crack during the daytime and nighttime, respectively. Fig 5.6(c) and 5.6(d) show the
data collected when the simulated crack is 2mm wide. The test results of 3 and 5mm
are similar to the 2mm case.
It was observed that, the 1mm wide crack was partially captured by the 3D
laser technology, and the 2mm wide crack was fully detected. Table 5.1 lists the
quantitative scores derived from the linear buffered Hausdorff scoring method for the
cracks with different widths under two lighting conditions. For cracks with widths of
1mm, scores are approximately 64. For cracks with widths equal to or greater than
2mm, scores are better, approximately 93. Daytime and nighttime tests resulted in
similar scores. The maximum score difference was 0.2. Thus the preliminary
controlled laboratory result demonstrated that the 3D laser system is capable of
detecting cracks whose widths are equal to or wider than 2mm.
Fig 5.4 A gap between two solid wood boards to simulate a crack
with known width (Source: Tsai and Li, 2012)
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Fig 5
Fig 5.6 Cr
24
.5 Two lighting conditions: (a) daytime (b
(Source: Tsai and Li, 2012)
ack segmentation results on simulated cra
(Source: Tsai and Li, 2012)
nighttime
ks
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Table 5.1 Scores for the controlled tests
(Source: Tsai and Li, 2012)
Score
Crack width
1 mm 2 mm 3 mm 5 mm
Daytime 63.9 93.6 93.1 93.3
Nighttime 64.1 93.4 93.0 93.1
5.3.2 Field test procedures
In addition to the controlled laboratory test, two field tests on actual
roadways were conducted. The first field test was to evaluate the potential of the 3D
laser system to detect cracks under low intensity contrast conditions. The second field
test was to evaluate the capability of the 3D laser system to detect cracks under
different lighting conditions, including nighttime, daytime with shadow, and daytime
with no shadow.
First test:
Fig 5.7(a) shows a roadway image with a low intensity contrast between a
crack, approximately 1 to 6 mm wide, and pavement background. The low intensity
contrast makes the crack difficult to be detected, even with the human eye. However,
the data collected using the 3D laser technology from the same area showed a more
distinct contrast between the crack and the pavement background. This is illustrated
by Fig 5.7(b) and 5.7(d) collected during day and night respectively. Fig 5.7(c) and
5.7(e) shows the corresponding crack segmentation results. The high scores from this
first test, namely 98.3 for daytime and 98.0 for nighttime demonstrated the potential
of the 3D laser technology for detecting cracks under low intensity contrast
conditions.
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Fig 5.7 Test results on a crack with low intensity contrast
(Source: Tsai and Li, 2012)
Second test:
The second field test was conducted on State Route (SR) 80 to evaluate
the consistency of using the proposed system in detecting cracks under three different
lighting conditions: nighttime, daytime with shadows, and daytime no shadows, as
shown in Fig 5.8. Eleven test segments, including 10 longitudinal cracks (cracks A to
J) and a transverse crack (crack T), were labeled in the field. Examples of the three
lighting conditions are shown in Fig 5.9. Fig 5.10 shows the 3D raw data collected
under three lighting conditions and corresponding crack segmentation results for the
crack J. Visual observation shows that the crack can be clearly captured by the 3D
laser system. The scores obtained are listed in Table 5.2. The three scores for each
crack were close to each other. The maximum difference among three scores was also
tabulated. The average score difference was found to be 1.9. The difference is very
small. Therefore, results of field tests demonstrated that the proposed 3D laser system
can perform consistently under different lighting conditions in the field.
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Table 5.2 Scores for the second field test
(Source: Tsai and Li, 2012)
Crack
name
Score
NighttimeDaytime with
shadow
Daytime with no
shadow
Score
difference
A 95.8 97.4 97.2 1.6
B 95.5 96.1 95.4 0.7
C 93.6 96.8 97.2 3.6
D 95.0 97.2 96.9 2.2
E 96.5 97.8 97.3 1.3
F 96.5 98.0 97.5 1.5
G 95.1 97.7 97.5 2.6
H 95.4 96.6 97.6 2.2
I 96.3 96.3 97.4 1.1
J 95.6 97.6 97.7 2.1
T 95.9 96.9 97.6 1.7
Average score difference 1.9
5.4 Findings and Concluding Remarks
Both controlled tests and actual road tests have demonstrated that it is
feasible to detect cracks under different lighting conditions and low contrast
conditions. Controlled tests showed that cracks with widths equal to or greater than 2
mm can be effectively detected from the pavement background, whereas 1 mm wide
cracks can be partially detected. The field tests showed that, for three lighting
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conditions, the average score difference is less than 2%. Thus the experiment shows
that the proposed 3D laser technology is very promising for crack detection.
6. CONCLUSIONS
Pavement distresses are contributed by several reasons. If not attended
properly, they may lead to progressive failure of the entire pavement. Hence there is a
high need for timely detection of pavement distresses. A 2D intensity-based imaging
system is the main data acquisition system that has been used for the past two
decades. Its intensity based data acquisition method makes it sensitive to lighting
effects. In general, the performance of distress detection is severely hampered in the
presence of shadows, lighting effects, non-uniform crack widths, and poor intensity
contrast between cracks and surrounding pavement surfaces. The shallow or thin
cracks are sometimes invisible to the 2D system. Manual inputs are required to adjust
the input parameters so that the algorithms can perform reasonably. Therefore, full
automation of pavement distress detection has remained a challenge especially for
accurate and reliable detection. With the advances in sensor technology, a 3D laser-
based pavement surface data acquisition system that can collect high resolution 3D
continuous pavement profiles for constructing pavement surfaces has become
available. This 3D laser system is different from current 2D intensity-based imaging
systems. First, the 3D laser-based system is not sensitive to lighting. Noise, such as oil
stains and poor intensity contrast, will not interfere with the segmentation algorithms
by using the acquired range data. As long as there is a distinguishable elevation
difference between a crack and its surrounding background, the segmentationalgorithm is able to capture the crack.
In order to understand the potential application of laser technology in distress
detection, two case studies were taken. The first study deals with the automatic
detection of potholes using laser-based optical system. The second study evaluated the
feasibility of using 3D laser technology to detect cracks under different lighting
conditions and low contrast conditions. Both studies showed that laser technology has
potential applications in timely and fully automated detection of distresses inpavements.
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REFERENCES
1. Chang, K, T., Chang J, R., Liu J, K. (2005). Detection of pavement distresses
using 3D laser scanning technology, ASCE 2005 International Conference on
Computing in Civil Engineering, Maxico, Cancun, July 12-17. pp. 1-11.
2. IRC: 82-1982. Code of practice for maintenance of bituminous surfaces of
highways.
3. Tsai, Y, C, J., Li, F. (2012). Critical assessment of detecting asphalt pavement
cracks under different lighting and low intensity contrast conditions usingemerging 3D laser technology.Journal of Transportation Engineering, Vol. 138
(5), pp. 649-656.
4. Wang, C, P, K. (2000). Designs and implementations of automated systems for
pavement surface distress survey. Journal of Infrastructure Systems, Vol.6 (1),
pp. 24-32.
5. Yu, X., Salari, E. (2011). Pavement pothole detection and severity measurement
using laser imaging.IEEE Journal. pp. 1-5.