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Abstract— The paper presents a Computer Vision System
based on texture segmentation and on a variation of the
Standard Hough Transform, in which the choice of the
parameters that determine the straight line that better
represents the image is based on the contour conditions of the
particular case of weld line detection on fuel storage tanks,
aiming to allow their correct detection by the computer vision
system even in the absence of the reinforcement structure,
usually found in those cases. The proposed vision system
provides the necessary information to keep a set of ultrasonic
sensors, used to inspect the weld line, in the necessary position
in order to improve the inspection reliability.
I. INTRODUCTION
he increasing search for security in the industrial sector,
together with the necessity of quality control, stimulates
the accomplishment of great investments in inspection. In
this context, automatic inspection systems had become a
reality in this sector. They made possible a more trustworthy
inspection, minimizing the human error and carrying out
such processes faster and with lower costs [1], [2].
This need is even more critical when considering the
inspection in storage tanks of harmful products to the health
and the environment. To guarantee the security of the
employees and to prevent aggressions to the environment,
without affecting the health of the population that inhabits
Manuscript received June 18, 2008. This work was supported in part by
CENPES/PETROBRAS, CAPES and CNPq. Lucas Molina is with the Electrical Engineering Nucleus of Federal
University of Sergipe – NEL/UFS, Av. Marechal Rondon, S/N, São
Cristóvão-SE, Brazil, 49100-000. He is also a Masters student at the COPPE of the Federal University of Rio de Janeiro – COPPE/UFRJ, Cidade
Universitária, Ilha do Fundão, Rio de Janeiro-RJ, Brazil, 21945-970 (e-
mail:lmolina@ufs.br). Elyson A. N. Carvalho is with the Electrical Engineering Nucleus of
Federal University of Sergipe – NEL/UFS, Av. Marechal Rondon, S/N, São
Cristóvão-SE, Brazil, 49100-000. He is also a PhD. student at the Electrical Engineering Departament of the Federal University of Campina Grande –
DEE/UFCG, Av. Aprígio Veloso, 882, Bodocongó, Campina Grande-PB,
Brazil, 58109-900 (e-mail: ecarvalho@ufs.br). Eduardo O. Freire is with the Electrical Engineering Nucleus of Federal
University of Sergipe – NEL/UFS, Av. Marechal Rondon, S/N, São
Cristóvão-SE, Brazil, 49100-000 (phone: +55-79-2105-6834; fax: +55-79-2105-6684; e-mail: efreire@ufs.br).
Jugurta R. Montalvão Filho is with the Electrical Engineering Nucleus of
Federal University of Sergipe – NEL/UFS, Av. Marechal Rondon, S/N, São Cristóvão-SE, Brazil, 49100-000 (phone: +55-79-2105-6834; fax: +55-79-
2105-6684; e-mail: jmontalvao@ufs.br).
Flávio de A. Chagas is with the Electrical Engineering Nucleus of Federal University of Sergipe – NEL/UFS, Av. Marechal Rondon, S/N, São
Cristóvão-SE, Brazil, 49100-000. He is an under-graduate student at the
NEL/UFS (e-mail: flavio.chagas@yahoo.com.br).
next to the industries, frequent inspections of such tanks are
carried out, over all in its weld lines, as it is the case of the
used spherical tanks in the gas storage.
The inspection of such tanks must to be made inside and
outside of them. When inspecting its interior, the
professional in charge for it is exposed to an even bigger
danger, as the environment may present traces of the
previously stored substance, thus the risk of explosion may
be imminent, and/or the environment atmosphere may be
toxic or with an insufficient concentration of oxygen to
support the human life.
The spherical tanks usually have big dimensions, forcing
the in-charged professional to work in high places, thus,
making the job even more dangerous and thus increasing the
costs to the company.
One way to carry out the weld line inspection is based on
the emission of high-frequency ultrasonic waves [3], [4].
The ultrasonic waves are emitted under different angles
through the reservoir structure, and propagate themselves
around it.
When air bubbles are inside the weld, the wave is
reflected, thus allowing for their detection. This kind of fail
detection is already being used and is not the focus of this
work. However, the reliability of this fail detection scheme
is hardly affected when the correct positioning of the set of
ultrasonic sensors is not guaranteed [3], [4]. This is the
reason that makes the correct detection of the weld line a
theme of major importance, and it is also the inspiration to
the realization of this work.
To carry out a reliable inspection it is necessary to exactly
identify the weld line, thus obtaining the necessary data to
position the inspection-sensor correctly.
The selection of the sensing methods to be used to
identify the weld line is of major importance to assure the
inspection reliability. In [5], an autonomous system to keep
the correct positioning of the inspection sensors with respect
to the weld line is proposed. The weld line detection, in this
case, is made based on the information provided by a linear
arrangement of distance infrared sensors. In the absence of
the reinforcement structure commonly found in weld line the
proposed sensing scheme is strongly affected, but the weld
line profile can still be correctly identified if visual
information is used instead of distance measurements.
In face of such necessity of the industrial sector, this
paper presents a sensing system based on computer vision to
allow the correct detection of the weld line even in the
absence of reinforcement structures.
A Robotic Vision System Using a Modified Hough Transform to
Perform Weld Line Detection on Storage Tanks
Lucas Molina, Elyson A. N. Carvalho, Eduardo O. Freire, Jugurta R. Montalvão-Filho, and Flávio de
A. Chagas
T
The paper is organized as follows: Section 2 is ab
classical method of straight line detection applied to images
of real weld lines; Section 3 is dedicated to the method
proposed in this paper, which takes into account the contour
conditions of the particular case of weld line detection.
analysis of the obtained results, conclusions and propositions
for the future are presented in Section 4.
II. STANDARD HOUGH TRANSFORM
The Standard Hough Transform (SHT)
method to detect curves in binary images, widely used and
studied by several authors. An overview about this subject is
found in [6].
The weld line may be considered as a straight line, which
can be equated in several ways. The most suitable one is the
parametric equation of the straight line:
θθρ sincos yx +=
Using this equation it is possible to write equations to
straight lines of any orientation, what is not possible when
using the general equation of the straight line:
baxy +=
To the straight lines parallel to the y axis,
is infinite and a mathematical description to them cannot be
obtained using the general equation. Therefore, the
parametric equation is commonly used in the
implementation of the SHT.
The input to the SHT is a binary image. A simple way
obtain it consists in the use of a thresholding algorithm, as
presented in [7]. To optimally determine such threshold,
according to the entropy criterion, will be used the method
proposed by [8], [9], based on the unidimensional entropy
maximization of the histogram of the gray level image.
gray level image of the weld line used to illustrate the results
of this work was captured using a VGA resolution camera
and is shown in Fig. 1.
The unimodal characteristic of the histogram of the gray
level image, presented in the Fig. 2.a, is common for images
of weld lines, where normally it has concentration of the
gray levels in a narrow band of values, making difficult the
segmentation, even using an optimal method for the choice
of a global threshold [8].
For the choice of the threshold, the method proposed by
Kapur [10] is applied to the histogram presented in Fig. 2a.
Firstly, the histogram is equalized according to the method
proposed in [11], in order to increase the contrast and to
facilitate the segmentation. If the gray level intensity of the
original image is treated as a random variable
probability of occurrence in the image of a given intensity
is given by:
The paper is organized as follows: Section 2 is about a
classical method of straight line detection applied to images
of real weld lines; Section 3 is dedicated to the method
proposed in this paper, which takes into account the contour
conditions of the particular case of weld line detection. The
of the obtained results, conclusions and propositions
RANSFORM – SHT
(SHT) is a classical
images, widely used and
authors. An overview about this subject is
The weld line may be considered as a straight line, which
equated in several ways. The most suitable one is the
(1)
Using this equation it is possible to write equations to
straight lines of any orientation, what is not possible when
using the general equation of the straight line:
(2)
, the coefficient a
is infinite and a mathematical description to them cannot be
obtained using the general equation. Therefore, the
parametric equation is commonly used in the
The input to the SHT is a binary image. A simple way to
obtain it consists in the use of a thresholding algorithm, as
. To optimally determine such threshold,
according to the entropy criterion, will be used the method
dimensional entropy
the histogram of the gray level image. The
gray level image of the weld line used to illustrate the results
of this work was captured using a VGA resolution camera
The unimodal characteristic of the histogram of the gray
2.a, is common for images
of weld lines, where normally it has concentration of the
gray levels in a narrow band of values, making difficult the
segmentation, even using an optimal method for the choice
For the choice of the threshold, the method proposed by
is applied to the histogram presented in Fig. 2a.
Firstly, the histogram is equalized according to the method
, in order to increase the contrast and to
ntation. If the gray level intensity of the
original image is treated as a random variable r, the
probability of occurrence in the image of a given intensity rk
( )n
nrp kkr =
where nk is the number of occurrences of the intensity
n is the total number of pixels of the image. Thus, a discrete
version of the Cumulative Distribution
given by:
( ) ( )∑ ===
k
j jrk rprFCDF0
And taking F(rk) as a function that maps each
into a new kr′ described by:
( ) ( ) ∑∑ =====
k
j
j
j
k
j rkkn
nrprFr
00'
where the new image formed by kr
image after the histogram equalization. The original
histogram, the equalized one and their respective CDFs are
presented in Fig. 2.
The resultant image after the histogram equalization,
considered by Kapur as necessary for a bette
threshold through unimodal histograms, is shown in Fig.
Fig. 2. a) Original image histogram; b) CDF
Equalized histogram; d) CDF of the image after the histogram
equalization.
Fig. 1. Example of a gray level image of a weld line.
(3)
is the number of occurrences of the intensity rk and
is the total number of pixels of the image. Thus, a discrete
Cumulative Distribution Function (CDF) is
(4)
) as a function that maps each rk of the image
(5)
kr′ represents the original image after the histogram equalization. The original
histogram, the equalized one and their respective CDFs are
the histogram equalization,
considered by Kapur as necessary for a better choice of the
threshold through unimodal histograms, is shown in Fig. 3.
CDF of the original image; c)
of the image after the histogram
Fig. 1. Example of a gray level image of a weld line.
The next step consists in binarize the image. Let
probability of occurrence of the gray level
maximization method is implemented through
search for the threshold T which maximizes the equation:
( ) ( ) ( ){ }THTHMAXT bp +=ψ
where:
( ) ∑ =
−=
T
i
iip
p
p
p
pTH
100
log
( ) ∑ +=
−=
G
Ti
iib
p
p
p
pTH
111
log
and
∑∑ +====
G
Ti i
T
i i pPpP1110 ,
11 1110 ==+=+ ∑ ∑∑ += ==
G
Ti
G
i ii
T
i i pppPP
where G is the number of distinct gray level intensities that
appear in the image, and Hp(T) and Hb(T
entropies of the sub-histograms generated by the threshold
The threshold T is tested to all possible values and for
each one a corresponding discrete function
The curve of ψ(T) can be seen in Fig. 4a, where it is possible
to notice the threshold T that maximizes the function (6) and
divides the histogram into two regions, binarizing the image
in an optimal way with respect to the entropy aspect and
considering the use of a single threshold
presented in Fig. 1 a threshold T=0.266 was found, and when
applied results in the binary image of Fig. 4b.
It is evident that for this kind of image a global threshold
is not the best solution, due to the unimodal nature of the
histogram and to its high sensitivity to illumination
problems. A search for multiple thresholds using more
advanced techniques of exploration of Histogram
those demonstrated by [12]-[14], which consider not only
the independent probability of each pixel, would produce
Fig. 3. Resultant image after the histogram equalization
The next step consists in binarize the image. Let pi the
level i, the entropy
rough an exhaustive
which maximizes the equation:
(6)
(7)
(8)
(9)
(10)
is the number of distinct gray level intensities that
T) are the partial
histograms generated by the threshold T.
is tested to all possible values and for
each one a corresponding discrete function ψ(T) is obtained.
, where it is possible
that maximizes the function (6) and
wo regions, binarizing the image
in an optimal way with respect to the entropy aspect and
considering the use of a single threshold. To the image
=0.266 was found, and when
applied results in the binary image of Fig. 4b.
is evident that for this kind of image a global threshold
is not the best solution, due to the unimodal nature of the
histogram and to its high sensitivity to illumination
A search for multiple thresholds using more
ation of Histograms 2D, as
which consider not only
the independent probability of each pixel, would produce
much superior results, but these algorithms are
computationally intensive and so
implemented and used on real-time applications, a very
important characteristic when considering the target
application of this work.
Finally, the SHT is applied [6] by
4b, as input, to produce the parametric space firstly
described by [15], in which another exhaustive search is
made. This time, to each white pixel of the image two
vectors of parameters ρ and θ are calculated. The values of
the former varies in the range between 0 and 180 degrees,
and to each value of θ is calculated a value
on the resolution adopted for θ, these calculus may demand a
high computational cost. To reduce the number of pixels to
be processed by the SHT, the contour extraction of the
binarized image is performed according to
resultant image is shown in Fig. 5a.
The parametric space in Fig. 5b is nothing more than a
bidimentional matrix of accumulators that indicate how
many votes each pair (θ,ρ) obtained in the process. The
accumulators are presented in Fig. 6 in the form of a 2D
histogram. As the objective is to find a unique straight line
that better represents the image, considering the classical
theory of the SHT, the parameters that determine this
straight line are the most voted ones. The resultant straight
line is shown in Fig. 7, plotted over the original image.
Fig. 6. Accumulators matrix presented like a 2D histogram.
Fig. 5. a) Contour extraction of the binarized image
Fig. 4. a) Function ψ(T) evidencing the threshold that maximizes the entropy of the histogram; b) Binarized image using
threshold T=0.266.
the histogram equalization.
much superior results, but these algorithms are
they are difficult to be
time applications, a very
important characteristic when considering the target
by using the image in Fig.
to produce the parametric space firstly
, in which another exhaustive search is
made. This time, to each white pixel of the image two
are calculated. The values of
the former varies in the range between 0 and 180 degrees,
is calculated a value for ρ. Depending
, these calculus may demand a
high computational cost. To reduce the number of pixels to
be processed by the SHT, the contour extraction of the
binarized image is performed according to [11]. The
The parametric space in Fig. 5b is nothing more than a
bidimentional matrix of accumulators that indicate how
obtained in the process. The
accumulators are presented in Fig. 6 in the form of a 2D
ram. As the objective is to find a unique straight line
that better represents the image, considering the classical
theory of the SHT, the parameters that determine this
straight line are the most voted ones. The resultant straight
over the original image.
Fig. 6. Accumulators matrix presented like a 2D histogram.
Contour extraction of the binarized image; b) Hough space.
the threshold that maximizes the
) Binarized image using the global
III. THE PROPOSED METHOD: A MODIFIED HOUGH
TRANSFORM
Despite of the efficiency of the method presented in the
previous section, based on the SHF, to detect straight lines in
images, it is very sensible to noises and illumination
problems, and thus, the result is strongly dependent of the
segmentation step.
The proposed method, to be presented in this section,
takes into account two intrinsic characteristics of images
from weld lines, its thickness and differentiated texture of
the weld line with respect to the image background.
The proposed segmentation is oriented to detect a kind of
texture which characterizes the weld line in the image,
producing an output image in the form of a unique elongated
object, with approximately the same thickness of the weld
line. In this object, a modified version of the Hough
transform, introduced in this paper, is applied, whose
parameters selected to represent the resultant straight line are
not the most voted ones in the generated parametric space.
Instead of this, the parameters are chosen searching for a
group of accumulators with similar number of votes to a
same θ, and selecting the θ with the smallest variance of ρ, thus allowing to search only for straight lines with a
minimum thickness, and discarding those ones whose
thickness is not compatible with thickness of a regular weld
line. This approach contributes to reduce the sensibility of
the Hough transform to noise and to improve the final result
without additional computational cost.
A. The Proposed Segmentation Method
The segmentation method here proposed is based on texture
information acquired through the second central moment
[11]. In a similar way that in the Section 2, the entropy
maximization of a unidimensional histogram is used, but this
time, instead of the gray level histogram of the image, a
vector representing a sampling of standard deviations of
non-overlapping regions, which cover the entire original
image. Such vector is named vσ. Each sub-region is defined by:
=
+++
+
sjsijsi
sjiji
k
pp
pp
SubR
,,
,,
L
MOM
L
(11)
where the kth sub-region has as first element, the pixel pi,j of
the image and the square dimension s. Considering SubRk as
an independent matrix of s dimension and elements denoted
by SRi,j, the kth element of vector vσ is given by:
( )∑ ∑= =−=
s
i
s
j kjik SRSRv1 1 ,σ (12)
where kSR is the mean of the elements of SubRk.
The distribution of vσ vs. the length of vσ, obtained from
the image shown in Fig. 1 is presented in Fig. 8, and its non-
equalized histogram in Fig. 9.
The probability of occurrence of a certain vσk is given by:
( )n
nvp k
kv =σσ (13)
where nk is the number of occurrences of the element vσk in
vσ and n is the total number of elements of vσ. From the non-equalized histogram shown in Fig. 9, and
using (6), (7) and (8), with the probability pvσ in the place of
pi, P0 and P1 are now calculated by:
Fig. 9. Non-equalized histogram of vσ.
Fig. 8. Distribution of vσ vs. the length of vσ.
Fig. 7. The resultant straight line obtained using the SHT plotted over
the original image.
∑ ==
T
i vpP10 σ
and ∑ +==
G
Ti vpP11 σ
where G is the number of columns of the histogram of
The threshold that maximizes the discrete
calculated in a similar way as presented in the previous
section and the due to this, detailed procedure will be
omitted. The discrete function ψ(T) obtained is presented in Fig. 10. As can be noticed looking at the figure, the optimal
threshold to vector vσ is T≅ 0.021. The application of such threshold to the image
similar way as that carried out to calculate vector
and (12), but this time, the sub-regions overlapping is
necessary to rebuild the image without losing informatio
To the kth pixel of the image, pi,j, (12) will be applied,
considering the sub-region SubRk as the region of interest,
with dimension s, and the kth pixel as its central element.
This sub-region may be described by:
=
++−+
+−−−
2,
22,
2
,
2,
22,
2
sj
si
sj
si
ji
sj
si
sj
si
k
pp
p
pp
SubR
L
MM
L
The value of vσi,j, calculated for a given pixel
(15) and (12), is then compared with the threshold
new is attributed to the image according with the rule:
Fig. 11. Binarized image resultant of the application of the
segmentation procedure here proposed.
Fig. 10. Function ψ(T) evidencing the threshold that maximizes the
entropy of the histogram of vσ.
(14)
is the number of columns of the histogram of vσ. e function ψ(T) is
calculated in a similar way as presented in the previous
section and the due to this, detailed procedure will be
) obtained is presented in
Fig. 10. As can be noticed looking at the figure, the optimal
The application of such threshold to the image is done in a
similar way as that carried out to calculate vector vσ in (11) regions overlapping is
necessary to rebuild the image without losing information.
, (12) will be applied,
as the region of interest,
pixel as its central element.
(15)
, calculated for a given pixel pi,j from
(15) and (12), is then compared with the threshold T, and a
is attributed to the image according with the rule:
<
>=
Tvif
Tvifp
ji
ji
ji
,
,
, ,0
,1'
σσ
The resultant image of this binarization process is shown in
Fig. 11, where it is possible to notice an elongated object
whose thickness is similar to the one of the weld line shown
in the image, as expected, showing that the proposed method
is capable to attain the aimed objectives.
B. The Modified Hough Transform
The Hough transform implemented in this work is a bit
different of its classical version due to some modifications
that were made taking into account the contour conditions
associated with the task of detecting weld lines in storage
tanks. Such modifications were implemented in a SHT, but
they could be also used in faster implementations of the
Hough transform, like the FHT (Fast Hough Transform)
since the modifications here proposed do not concern the
parametric space generation, but in the selection of the pair
of parameters (θ,ρ) which define a straight line in the image.
The same procedure described in Section
using as input the image presented in Fig. 11 (obtained using
the segmentation method proposed in this paper), is then
used to obtain the parametric space of Hough shown in
Fig.12. In this case, not just one accumulator, but a set of
them are evidenced.
Each vector θi (composed by each column of the
accumulators matrix), independently,
process with a window function, searching for
amplitude window which represents it, and adopting as a
minimal width to the window ¾ of the estimated width of
the weld line, thus neglecting the isolated stra
whose thickness is incompatible with the weld line
dimensions. This procedure reduces the sensitivity to biased
noises, and it is illustrated in Fig. 13 for two distinct vectors
θi and θj.
Fig. 13. The fitting process with a window function applied on two
distinct vectors θi and θj.
Fig. 12. The parametric space of Hough.
Fig. 11. Binarized image resultant of the application of the
the threshold that maximizes the
(16)
resultant image of this binarization process is shown in
Fig. 11, where it is possible to notice an elongated object
whose thickness is similar to the one of the weld line shown
in the image, as expected, showing that the proposed method
ain the aimed objectives.
The Modified Hough Transform
The Hough transform implemented in this work is a bit
different of its classical version due to some modifications
that were made taking into account the contour conditions
detecting weld lines in storage
tanks. Such modifications were implemented in a SHT, but
they could be also used in faster implementations of the
Hough transform, like the FHT (Fast Hough Transform),
since the modifications here proposed do not concern the
parametric space generation, but in the selection of the pair
) which define a straight line in the image.
The same procedure described in Section 2, but this time,
using as input the image presented in Fig. 11 (obtained using
tation method proposed in this paper), is then
used to obtain the parametric space of Hough shown in
. In this case, not just one accumulator, but a set of
by each column of the
ependently, is submitted to a fitting
window function, searching for the highest
amplitude window which represents it, and adopting as a
minimal width to the window ¾ of the estimated width of
the weld line, thus neglecting the isolated straight lines
whose thickness is incompatible with the weld line
dimensions. This procedure reduces the sensitivity to biased
is illustrated in Fig. 13 for two distinct vectors
The fitting process with a window function applied on two
In the previous step, a valid observation window is
determined to each vector θ. Then, the search for the window with the lower variance in the related values of ρ is
made, with the objective to find a similar voting for different
values of ρ in the same θi. The value of θ with the lower
variance in the observation window and that at the same
time has a minimum amplitude of considered voting is the θ
that better represents the weld line in the image, and the
corresponding value of ρ is the average point of the
observation window that got the lower variance in the
previously described analysis.
The straight line obtained applying the method described
in this section is shown in Fig.14.
IV. CONCLUDING REMARKS
This paper presented a new approach for the extraction of
the parameters that define the straight line that better
represents the weld line in an image. An image segmentation
procedure based on texture and a variation of the Hough
Transform was proposed to improve the system’s noise
immunity. The obtained results showed the effectiveness of
the proposed method, even for images with illumination
problems and histograms with unimodal characteristic, as it
is the case of the majority of the images from weld lines of
storage tanks, without the necessity of histogram
equalization, however, it is necessary to test the algorithm
for a bigger and different image data base, to verify the
method’s robustness and conclude about it generality.
The described method presents an improvement in the
algorithm execution time and computational cost due to the
fact that in the proposed segmentation, the used vector vσ has a dimension much smaller than the vector r, the gray
level vector of the image, used in the classic method that,
moreover, still needs a histogram equalization stage,
unnecessary in the considered method. On the other hand,
for the proposed method, it is enough to make use of a not
overlapped sampling rate of sub-regions, thus reducing the
time needed to find the threshold to be used. In both cases,
the global threshold is determined through the entropy
maximization of the described unidimensional histogram
[10]. The binary image, resultant of the entropy
maximization of the gray levels histogram, revealed itself
very sensitive to illumination problems and noisy images.
However the binarization from vector vσ presents more clear
results, good enough for correct weld line detection, as can
be noticed by comparing the results presented in Fig. 7
(Section 2) and Fig. 14 (Section 3).
Despite of the existence of alternative implementation
forms of the SHT, like the RHT and FHT [7], the Hough
transform still has a very high computational cost and is very
sensitive to imperfections in the image segmentation stage.
The proposed modifications reduces the sensitivity to
illumination problems and biased noisy, and also can be
used in faster implementations of the Hough transform, like
the FHT, for example, keeping its contribution and speeding
up the process even more.
As a future work, a mobile robot will be equipped with a
camera and the proposed approach should be applied to
acquire the necessary information to feed the control system
used to keep the track of the weld line when performing a
real inspection task.
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Fig. 14. The resultant straight line obtained using the proposed
method plotted over the original image.