AUTOMATIC CRACK DETECTION IN THERMAL IMAGES
FOR METAL PARTS
S. Ghidoni, M. Minella, L. Nanni, C. Ferrari, M. Moro, E. Pagello, E. Menegatti
Department of Information Engineering
University of Padova
Via Gradenigo 6/A I-35131 Padova, Italy
ABSTRACT. In this paper a system for automatic crack detection is presented. The system is
capable of analyzing metal parts by means of a laser excitation system and a thermographic
camera. The laser creates thermal gradients inside the part under inspection, and the thermal
camera observes how heat diffuses inside the part. Cracks are automatically detected thanks to
computer vision algorithms specifically developed for this task, that are capable of measuring
and classifying heat profiles. Different algorithms have been developed for rugged and
smooth metal parts, since the reaction to laser excitation is rather different. The detection
algorithms have been tested on several sequences and showed very good detection
performance also with cracks of very small size, having a width of 120 µm.
INTRODUCTION
Quality inspection at the end of a production line is an important stage in industry,
especially for high-performance components. Parts undergoing strong mechanical and thermal
stress should be carefully checked, since small defects can affect performance and reliability
of a component. Crack detection is one of the most common checks to be performed, because
cracks are a common source of failure, and they affect a high number of different productions.
For metallic parts, crack detection is still performed exploiting a technique called
“magnetic particle inspection” (MPI): the part to be analyzed is first washed, then put into a
magnetic field and finally covered with magnetic particles, either in the form of a dry powder,
or, more frequently, in a wet suspension. Cracks are easily detected because they cause leaks
in the magnetic flux; such leaks are highlighted by the particles, which can be inspected by
means of a UV light. The whole process is very complex and needs to be done manually; it is
also extremely time-consuming, because parts need to be cleaned, magnetized, covered with
particles, inspected, de-magnetized and cleaned again. Moreover, magnetic particles and their
carrier are a source of pollution, and should be properly processed after use.
Given the complexity of MPI, a method for simplifying the process of crack detection and
making it automatic is highly desirable: investigation on this topic is the aim of the
ThermoBot project. The main idea is to exploit thermography instead of magnetic particles to
detect cracks, and to apply this method to parts made of non-metallic materials, like carbon
fiber. Inspection is performed by means of a laser and a far infrared (FIR) camera (also called
thermal camera or thermocamera), that observes how the heat carried by the laser diffuses
inside the part: since cracks cause alterations on the heat flux, these can be exploited to detect
cracks. Inspection of parts with complex geometry requires the laser and thermocamera to
frame the object from many different viewpoints: the demonstrators developed for the project
are therefore built around a robot that is able to move the part under inspection in front of the
acquisition system.
In this paper a system for detecting cracks in metal parts is described. It was developed as a
part of the project ThermoBot (www.thermobot.eu) funded by the European Commission in
the Factory of the Future research program. The Thermobot inspection system is based on the
analysis of the laser spot that is going over the crack. The system was tested on a number of
metal sample parts worked with different finishing processes, namely smooth and rugged, in
order to assess the performance obtained on different surfaces.
STATE OF THE ART
The topic of crack detection has been tackled in a number of different ways in the
literature, given the strong importance of this type of quality check. A variety of approaches
have been used, like the propagation of ultrasounds that is used in [1] to detect cracks and
lamination defects in metallic pipes, or Eddy currents [2,3,4]. Other methods exploit magnetic
cameras to detect crack in parts that are at high temperature [5], or magnetic flux leakage[6],
while the method described in [7] studies the heat produced by the Joule effect.
Methods based on image analysis have also been exploited in the literature, ranging from
detection of welding defects in pipelines [8] to concrete surface analysis [9] and the protection
of cultural heritage [10]. Thermographic image analysis systems have recently been proposed
for performing in-situ non-destructive inspections during thermomechanical fatigue tests [11];
the system showed a high sensitivity, being able to detect cracks smaller than 500 µm. The
system proposed in [12] is slightly different from the others discussed above as it is meant to
inspect different types of materials during fatigue tests, and detect the cracks as soon as they
appear.
Thermography-based crack detection is often coupled with excitation methods like eddy
currents [13] or laser beams; in particular, lasers provide the inspection process with high
flexibility, as it is possible to concentrate the heat on a small spot, and enabling and disabling
the heat source can be done instantly, generating pulses at high frequency. This last
characteristic is exploited in pulse thermography and techniques that are derived from it [14].
Another technique based on laser technology is the “flying spot active thermography” [15],
that refers to a laser spot that causes a local excitation on the part under inspection. This is
similar to the analysis method employed in the ThermoBot project, and was chosen in [15] to
inspect high pressure turbine blades.
In the following, two techniques for detecting cracks in metal parts will be described. They
were developed for addressing the task of automatic crack detection in parts with different
characteristics and with different thermal excitation methods. Both smooth and rugged metal
parts will be considered, excited using both pulsed and continuous heat source.
ANALYSIS OF SMOOTH METAL PARTS
Inspected parts
Parts that were considered for testing the inspection system belong to two categories:
 Test part A: metal discs, composed of eight blades made of smooth and reflecting
metal;
 Test part B: portions of a crankshaft, made of rugged metal, and characterized by a
more complex geometry.
Even though both test parts are made of metal and the physical principle on which the
detection is based is the same for both, acquired images are very different, because of the
different surface working.
Test part A is the simplest case: the flat and mirror-like surface provides very uniform
images, that are easy to process. A laser beam that hits the part saturates the camera, and
appears as a white spot surrounded by a region of decreasing gray levels, caused by heat
diffusion from the hot spot. The crack detection algorithm for smooth metal parts like test part
A is based on the analysis of the region where heat diffuses.
The analysis of the heat diffusion region is performed in three steps:
 Hot spot detection,
 Radial gradient analysis,
 Tangential gradient analysis.
As a first step, the hot spot needs to be detected. This is rather easy as the laser spot is the
portion of the image with highest temperature, and is always saturated in all working
conditions we observed. It is also reasonable to assume that this is true in most acquisition
setups, therefore hot spot detection is performed with a simple algorithm. A dynamic
threshold, depending on the lowest and highest temperature values found in the image, is first
applied to obtain a binary image in which the laser spot is the only white element. A dilation
operator is then exploited to smooth the shape obtained by thresholding; the centroid is finally
evaluated as the center of mass of the resulting shape.
Once the shape and location of the laser spot is available it is possible to analyze the
surrounding area to perform crack detection.
Radial gradient
The first analysis involving the heat flux region is performed on its radial gradient. While a
normal gradient operator considers pixel difference only along the horizontal and vertical
axes, the radial version considers multiple directions intersecting in a central point, that
corresponds to the hot spot centroid in our case.
Radial gradients are usually evaluated by comparing points that are aligned along a given
direction intersecting the central point. However, when this is evaluated in the discrete
domain of an image, an important side effect should be considered: the number of pixels at a
given distance to the center is not constant, but depends on the distance itself. This is
important in performing comparisons: for example, in [16] an approach is proposed that
instead of comparing pixel values focuses on image areas of variable size, depending on the
distance to the center.
Our approach to the problem is slightly different, and moves from outer regions towards
the center. Consider the image portion of fig. 3 (left): the center is represented by the green
spot at the bottom left corner. For each pixel, a vector connecting it with the center is shown,
whose color is red if it crosses one pixel only, and in blue if the crossed pixels are two. It is
important to separate these two cases as pixels with a red vector can be compared with one
other pixel (indicated by the vector), as it happens in any other gradient evaluation algorithm.
Pixels with a blue vector need to be compared to more than one other pixel, namely the two
that are intersected by the vector pointing to the center, as highlighted in fig. 3 (center). As it
can be seen from the figure, each pixel in the image is compared with no more than two other
pixels, which solves the issue of considering image portions of increasing area described in
[16].
Figure 1. Radial gradient schemes.
Using the method described above four different situations can be recognized, that are
summarized in fig.3 (right). The pixels PA, PB and PC are connected to the center along a
vertical, diagonal and horizontal direction respectively; for this reason, the radial gradient is
evaluated by comparing them with one pixel, that is the one pointed to by the vector. In the
case of PD, the line connecting to the image center has a stronger vertical component: PD
should therefore be compared with the pixels placed along the down and down-left directions.
The case of PE is specular. The values that the radial gradient will assume are therefore:
PA '  P( X A , YA  1)  PA
PB '  P( X B  1, YB  1)  PB
PC '  P( X C  1, YC )  PC
PD '  f ( P( X D  1, YD  1), P( X D , YD  1)  PD
PE '  f ( P( X E  1, YE ), P( X E  1, YE  1)  PE
where P ( X , Y ) is the point at coordinates ( X , Y ) , and f () is a function that controls the
different weight of the neighboring pixels.
Tangential gradient
The tangential gradient provides complementary information with respect to the radial
version, and it is therefore important to fully describe the area surrounding the laser spot. The
principle on which it is evaluated is similar to what was described for the radial gradient, with
the only fundamental exception that in this case vectors do not lie on the direction connecting
each pixel to the spot center, but are perpendicular to such direction. The scheme in fig. 5 has
the same meaning of fig. 4 in this new context.
Figure 2. Four types of pixel comparison for the tangential gradient algorithm.
Radial gradient equalization
The gradient evaluated on the area surrounding the laser spot is not uniform, and it is
higher in the locations closer to the spot, as it is easily understood by considering the heat
transmission inside metal parts. From the image processing point of view, this breaks the
homogeneity of the region being inspected, and should therefore be contrasted. To balance
this effect, we propose a correction called radial gradient equalization (RGE), that is an
amplification of the gradient that depends on the distance to the centroid of the laser spot. The
equalization is applied in a region surrounding the laser spot until a maximum length defined
by the parameter LMAX . The amplification is defined as a function of the distance to the
centroid and increases linearly from the value G MIN until a certain value GMAX that is reached at
the distance LLIM , then the gain saturates:
l  LLIM
 G  l
RGE (l )   MIN
LLIM  l  LMAX
GMAX
Results obtained applying the RGE can be seen in fig. 6: the cracks at the bottom edge are
highlighted after applying the equalization (right) with respect to the original image (left).
Figure 3. Comparison of the gradient image before (left) and after (right) the application of the RGE.
Edge-based crack detection
The proposed algorithm for detecting cracks is meant to analyze the images of the radial
and tangential gradient, and has better performance when equalization is adopted. To detect
cracks, the gradient image is divided into smaller parts (patches) that are analyzed separately.
Each patch is binarized by means of an adaptive threshold that depends on the average pixel
value. Some image enhancing functions are then applied, and finally cracks are detected
selecting the contours in the image having a significant size, discarding the one containing the
laser spot. The final algorithm for crack detection is rather simple, since it operates on images
that are strongly enhanced at lower level.
An example of crack detection can be seen in fig. 7, where the binarized image is shown
(left) together with the final result (right).
Figure 4. Example of image binarization (left) and final result (right) of the system
ANALYSIS OF RUGGED METAL PARTS
The algorithms described so far do not show good performance on rugged parts, because
the detailed analysis of radial and tangential gradients suffer from the noise generated by
rugged surfaces. A different approach has been developed to tackle this case, which is based
on radial density profile (RDP) [17], a method that has been used to characterize medical
images containing viruses. Such medical images are rather similar to the case of laser spot
analysis as in both cases it is important to study the circular shape of an object. The RDP
features are used to train a support vector machine [18], which is state-of-the-art among the
machine learning classifiers.
While the algorithm previously described can be seen as a “direct method”, since it is
aimed at detecting image elements that have the shape of a crack, the method based on RDP is
“indirect”, because it detects cracks analyzing the evolution of the shape of the laser spot
while it is heating a crack. The heat source was not pulsated in this case, as it would be
impossible to study the shape of a pulsating laser spot.
DISCUSSION
The approaches presented in this paper have been tested on several sequences. Tests
involved crack detection both on single images and in whole sequences.
In the case of smooth metal parts, only few images showing a crack were present in the
dataset, that was made of a limited number of pictures. Tests showed a good performance of
the algorithm, that was able to detect cracks in the region around the laser spot. However,
cracks cannot be detected when they are too close to the laser spot itself, because this is
masked by the algorithm, making any detection not possible. This means that in 38% of the
images the crack is not detected, but the same crack was previously detected in other images,
when it was located further from the laser spot. Considering the whole sequence, the
algorithm showed optimal performance, because it detected all cracks in the sequences, but
the dataset is still too small to thoroughly assess the system.
Performance of the crack detector for rugged metal parts was studied in more detail, thanks
to a much larger dataset. The system was tested on 31 sequences, each one framing the laser
going twice on a crack; all sequences were taken with the same sample part. The crack had
very small dimension, having a length of 8.36 mm length and a width of 120 µm only.
The sequences are divided into two sets:
 In set A, the laser power is kept at a given value of 7.5 W and the laser speed
changes in the range [60-200] mm/s, with a step of 10 mm/s between two consecutive
sequences;
 In set B the speed is kept at the value of 60 mm/s while the laser power takes
values in the range [5-20] W with a step of 1W.
As a testing protocol we chose a leave one out set protocol, that is, we trained the classifier on
the features extracted from set A and tested it on set B, and vice-versa. Tests resulted in an
average area under the ROC curve of 0.9337. The resulting DET-curve [19] is reported in fig.
7, which shows the good performance achieved by the system, considering that only one
sample part acquired changing laser power and scan speed was employed for training and
testing.
Figure 5. DET-curve resulting from tests performed on crack detection using RDP.
CONCLUSIONS
In this paper a system for automatic detection of cracks in metal parts was presented. The
system is based on thermographic analysis of the part under inspection and exploits a laser
excitation, either pulsed or continuous. The system exploits different algorithms depending on
the metal parts to be inspected, that can be either smooth or rugged, and is able to detect
cracks that are extremely small: the smallest detected crack has a width of 120 µm only. Even
though the work is still in progress, a first performance assessment showed good results,
indicating the approach is promising. Future works include the development of algorithms for
thermographic image processing for detection of defects on thermographic images obtained
with flash thermography.
ACKNOWLEDGMENTS
The research leading to these results has received funding from the European Union Seventh
Framework Programme (FP7/2007-2013)under grant agreement No. 284607.
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