VISUALIZATION AND DETECTION OF SMALL DEFECTS ON
PAINTED OR VARNISHED SURFACES
A. Gil, N. Montés, M. Mora, J. Tornero
Dto. Ingeniería de Sistemas y Automática
Universidad Politécnica de Valencia
46022 Valencia (Spain)
Abstract
Surface defect inspection is a very common task that
is performed in the industry as a final quality step of
manufacturing processes. Despite that a lot of care is
taken in order to avoid that these small defects
appear, they often are visible just after the painting
or varnishing is applied. A new technique shown on
this paper allows us a successful inspection on these
type of surfaces. A fast computer vision algorithm is
also presented. This algorithm is specially useful for
small defects or low resolution defects detection
providing a high robustness and performance when
critical processing time is required.
Key Words: Defect detection, Surface inspection,
Defect detection algorithm, Defect pattern, Surface
quality control.
I.
INTRODUCTION
Many surfaces, like metals or wood, are processed by
sanding, polishing and at the end they are painted or
varnished. During these processes it is not possible to
completely control the working environment, and
because of this small particles (some times invisible)
appears visible defects after one or often several
layers of painting or varnishing.
We propose the use of high frequency fluorescent
lamps, as it will be explained later, as a solution for
the visualization of small defects on painted or
varnished surfaces.
Different works related with this topic have been
published. Reynolds et Al [4] used a surface
inspection technique based in a double-pass
retroreflection. In [5] defects are visualized by using
a combination of the advantages of fringe projection
and grid reflection techniques.
Other techniques that include the use of luminaries
have been used before in some inspection systems for
quality control. The company Hubbell lighting has
developed a lighting system [2] that highlights
minute peaks, dents, valleys and ridges by increasing
contrast around these defects on specular and semispecular surfaces. The principle behind this detector
system is its generation of high contrast images on
the surface. Surface defects are highlighted at the
edge of the luminarie image reflected by the surface.
It is based in a high contrast between the luminarie
and background light. They assure that their Surface
Inspection Luminarie System improves the detection
of paint finish defects by as much as 20% compared
to conventional recessed lighting systems. It is
designed for being used for inspectors, helping them
to eliminate visual fatigue, but it has not been
integrated into an automatized system.
In [1] it is designed an automatized system for
quality control of cast aluminium alloys. They use
high frequency fluorescent lamps with diffusers for
visualizing the defects and use enhance algorithms
for defect detection. The defect visualization is done
again at the edge of the luminarie.
Fluorescent tubes have also been used in quality
inspection of tiles. In [6] a linear camera is used to
perform high quality images as one of the steps of an
automatized integrated system for defects detection.
Our system use high frequency fluorescent lamps in a
different way, making the defect visible in the centre
of the reflecting region on the surface instead of the
sides which produces an important amplification
effect.
This paper is organized as follows. In section II we
explain which is the methodology used for the
visualization of the defects. Experimental results are
presented in section III where we determine the limit
conditions of this technique. In section IV we obtain
experimental results. A new detection algorithm is
explained in section V. Conclusions are explained in
section VI.
II.
DEFECT VISUALIZATION
METODOLOGY
The philosophy of this method is that a light beam
that reflects by a defect is disturbed and goes into a
different direction than one that reflects straight away
by the surface without defect. This basic
phenomenon, that is explained by the Snell laws, is
shown in Figure 1.
Figure 3: Angle of the dark conical area.
Where:
DIS Distance between the camera and the defect.
PIX  Pixels occupied in the camera by the defect.
TAM  size of the defect.
α  angle of this cone.
Figure 1: Light effect when a defect is found.
This can be implemented by illuminating the surface
with a laser line, and studying the reflected beam,
which could take a lot of time for relatively small
inspection surfaces.
We propose the use of common high-frequency
fluorescent lamps, which are very cheap and easy to
find. A camera sited opposite to the light senses the
reflected light. It is important to state that the camera
senses the reflection of the light, so this method is not
valid for non reflectant surfaces.
   PIX  TAM 

2  DIS


FOV

CCDPIX
  arctan 
(1)
(2)
Where:
CCDPIX  Number of CCD pixels in this direction.
FOV  Field of view in this direction.
Δ  Resolution.
Amplification effect is, see Figure 4
As it is explained in [2] the use of reflected light
improves human inspection on painted surfaces. This
is because it produces an amplification effect. This
effect is produced because the reflected light
provokes a dark conical area. See Figure 2
Figure 4: Amplification effect.
Amp 
Figure 2: Dark conical area.
The angle  of this conical area is easily calculated.
See Figure 3.
TAM '
TAM
Where:
Amp Amplification factor.
TAM’  size of the amplification defect .
(3)
III. EXPERIMENTS
In order to study the behaviour of the light in defect
visualization, and to determine Amp and α
parameters several experiments have been made. For
this experiments we use a fluorescent lamp of 25 mm
diameter, a CCD camera of 640x480 pixels. We
select a minimum defect size of 0.3 mm2. From our
experiments we have found that there are some key
parameters to obtain a good defect visualization:
-
Influence of the
fluorescent lamp:
intensity
of
the
The intensity of the lamp must be set up properly
according to the reflection features of the
inspection surface. The exposure time feature of
the camera must be set up as well for the same
purposes. Due to the high acquisition frequency
of the cameras, it is strongly advisable to use
high frequency fluorescent lamps. Otherwise, if
a common 50-60 Hz lamp is used, different
intensities of light will be reflected on the
images taken, and they will be more difficult to
process.
-
Angle of incidence of the light beams
respect to the inspection surface:
Defect visualization varies depending on the
angle of incidence of the beams into the surface
from the fluorescent lamp. The more vertical the
camera the better the defect visualization. Of
course, is not possible to get reflected beams
from the surface area that is situated vertical to
the camera.
Figure 5: Angle of incidence of the light beams
respect to the inspection surface.
To determine the influence of this angle, we
developed an experiment by the use of two
articulated robots. Varying  angle in increments of
5º from 15 to 80 degrees, we get the minimum grey
level contained in the defect, and we determine if this
defect is detectable based in our own defect detection
algorithm explained in section V. The results are
shown in Figure 6.
Figure 6: Effect of the angle of the camera respect to
the grey level of the defect on the image.
Optimal defect visualization and detection is defined
in a certain range of angles from 89 to 60 degrees
(respect to the horizontal) . Defects are still visible
and detectable from 60 to 45 degrees, but its
visualization is not so good, and it decreases around a
30 per cent. Smaller angles than 45 degrees make the
visualization either uncertain or impossible and not
detectable based in our own algorithm (section V).
Also the size of the defect change respect to  angle.
Figure 7 show the results of this experience.
Figure 7: Area defect respect to the angle
-
Influence of the distance/width of the
light lamp from the surface:
The distance of the light is another parameter that
must be established properly. This distance and the
width of the fluorescent lamp define the width of the
reflected beam on the surface. In this experience we
use two articulated robots which move the
fluorescent lamp and camera in distance increments
of 5 cm with a fixed  angle of 80º. We also
determine if the defect is detectable or not, see Figure
8.
We studied this phenomenon around the visibility
border. Seeing Figure 8, with our camera and for the
minimum size defect established (0.3 mm2), the
visibility border is in (125,55) cm. With this distance,
the field of view is 60x40 cm. This establishes a
minimum  to be able to visualize the minimum
defect with two pixels using equation (2):

600
 0.93mm
640
according to equation (1)  is:
  0.08º
and using equation (3) the amplification for this size
of defect is:
Amp  6
These parameters can be obtained analogously for the
other direction.
Figure 8: Experimental results from different
distances of the camera and fluorescent lamp to the
defect.
There is a limit point situated at (125,55)cm. This is
an optimal point because we have the biggest field of
view possible.
IV. EXPERIMENTAL RESULTS
There are several phenomenon that appear depending
on the situation of the light line on the surface respect
to the defect.
When taking images at high resolution (like in Figure
9) , it is possible to see the defects in dark colour
inside a line with clear colour (which is the light line
reflected on the surface). Main advantage of this is
that the contrast of the defect is very high. This
makes much easier the defects to be distinguished
than applying other sort of frontal illumination.
Figure 9: Defect visualization at high resolution.
Figure 10: Defect size on the camera for a 0.3mm
defect for different distances of the camera. Theoretic
and experimental results.
Figure 10 compares how big the defect appears on
the image using our method and how big the defect
should appear according to the standard vision
systems (for the conditions mention before). We can
obtain the variation of the amplification for different
camera distances. See Figure 11.
Figure 11: amplification for different distances of the
camera comparing with theoretic values.
If we see Figure 13, it is possible to understand how
difficult is to select the number of the pixels that are
occupied by a defect, specially when the distance of
the camera and lamp light varies. We assume that
the variation of the amplification effect respect to the
distance of the camera is linear, obtaining the
equation :
Amp  0.02  DIS  1.7
Generic equation is:
PIX  (0.02  DIS  1.7)  (
curious feature: there is a gradient of light around the
defect in the same direction of the light line. This is
because the light beams from the light line
concentrates on the borders of the defects. this is
opposite to the effect produced in the centre of the
defect (dark conical area). The most important is that
this gradient of light follows the same structure for
every size of defect.
TAM  f  CCDPIX
)
DIS  TAMpix
Where:
TAMpix  is the size of each pixel on the CCD.
This model is shown in Figure 12.
Figure 13: Zoom of a defect visualization at low
resolution.
This feature, can be used successfully for fast defect
detection.
Our algorithm is based on a pattern that is
associated with every size of defect. An important
advantage from this method is that we get the size of
the defect as an output from the algorithm without
any other additional computing.
Figure 12: comparative between the theoretical
model and practical results.
At the limit point we obtain the best amplification
value: 6.46, which is a very good approach to the
value obtained using equation (2). This establishes
that the best performance is obtained at the limit
point.
V.
DEFECT
ALGORITHM
DETECTION
In order to get the detection process automatized a
fast algorithm has been developed. The main
difficulty when trying to detect defects is that they
appear inside the reflection line on the surface. There
are a lot of methods that can help us to localize the
light line. Knowing where the light line is in the
picture, we could apply specific techniques to
localize the defects inside the line. The problem is
that localizing the light line takes usually more time
that trying to localize the defects straight away.
Observing the defects carefully we see that because
of the way the light is applied, it appears a very
This pattern is one-dimensional, so it can run very
fast. The algorithm checks every line of the picture
looking for that particular pattern.
In order to avoid false defect detection problems on
the line border, a laplacian filter is applied after
finding a possible defect, so it can be determined
whether the defect is just a part of the border of the
light line or is a real defect. This check doesn’t take a
long time, because it is performed just when a
possible defect is found, not all over the image.
Another very important advantage of this method is
that the pattern can be applied to any colour surface.
It means that there is no need for a colour detection,
which makes this method very flexible and reliable
for automatized defect detection when different
colour surfaces are used. This is possible because the
pattern doesn’t depend on the colour.
As an example of a pattern, the defect shown in the
picture above can be detected using this pattern:
Ref
+
+
-
-
-
+
+
|
OR
|
AND
|
OR
It means that starting from a pixel of the image as
reference ‘Ref’, at least one of the next two pixels
(Ref+1, Ref+2) must have a higher grey level than
the reference. The following three pixels (ref+3,
Ref+4, Ref+5) must have a lower grey level than the
reference. At least one of the last two pixels (Ref+6,
Ref+7) must have a higher grey level than the
reference.
It is possible to define other restrictions, like for
example a minimum grey level for the reference or a
maximum grey level for the central pixels. This is
especially useful when the parameters of the
application are well known, so it will save processing
time. It is also possible to give weights (inside a
range) for every pixel respect to the reference grey
level for more accurate pattern searching.
Figure 14 shows the results of applying the algorithm
to the image shown before. We can see in the figure
on the left that the pattern for three pixels defects has
been found three times for the same defect in three
different lines. Another pattern for two pixels defects
has been found once at the first line of the defect.
The border of the light line hasn’t been a problem
because a laplacian filter has been used.
The time taken to process a 640x480 pixels is less
than 90 milliseconds on an Intel Pentium IV 2,4Ghz
CPU. Which means that at least 11 images can be
processed in a second.
Obviously if the defect is very thin, it is possible to
detect it just in one direction.
VI. CONCLUSION
The defect visualization technique presented on this
paper performs an excellent defect visualization on
painted or varnished surfaces, producing an
important amplification effect. It makes that this
method is especially useful when small defect
detection is required because the amplification effect
permits us the use of a low resolution camera.
All important parameters that have influence over the
defect visualization have been exposed and their
restrictions have been presented.
A fast defect detection algorithm has been also
presented. It is based on a pattern that is associated
with the size of the defects. Two important
advantages of this algorithm are that the size of the
defect is known straight away and that the pattern can
be applied to any colour surface.
VII. REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
Figure 14: Same defect detected in different
orientations.
As we can see in Figure 14, the same defect
processed with different orientations can be detected
by different size patterns, but the results are similar.
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Aluminium Alloys, 1996. More information http://
www.elai.upm.es/spain/proyectos/BRITE4336.htm
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