Digital Image Processing in the Diagnosis of Cracks in Steel Sheet

Taynara Martins Gonc¸alves

1 a

, Andrea G. Campos Bianchi

1 b

and Glauco F. Gazel Yared

2 c

Department of Computing, Federal University of Ouro Preto, Brazil

Department of Electrical Engineering at the Federal University of Ouro Preto, Brazil

Keywords:

Contour Segmentation, Automatic Detection, Computer Vision, Rail Inspection.

Abstract:

The railway is an essential part of the marketing chain. If it is considered efﬁcient, safe, and competitive,

on the other hand, the railways suffer from the enormous difﬁculty of maintenance due not only to their

great extension, dispersion and lack of ﬁnancial investments. Initiatives for automatic maintenance inspection,

mostly done manually, require development and consolidation. Therefore, this work presents a method for

identifying defects in sleepers based on analyzing images. We will use digital image processing techniques

that will allow us to extract the contours of the sleepers and therefore analyze their curvature. The development

is carried out with images from laboratory tests, not previously classiﬁed but subject to noise. The method is

validated through analysis with an image bank with about 20 images of defective and ﬂawless sleepers, with

an average assertiveness of 94.24%. The detection, classiﬁcation, and localization of faults in train tracks are

then investigated and discussed.

1 INTRODUCTION

The expansion of the use of the railway system, to-

gether with the speed, frequency, and weight per axle

adopted, directly affects the structure of the railway

lines, including rails, sleepers, and other components.

In this context, the sleepers play an essential role in

the stability of the rails, as they are responsible for

transferring the efforts produced by the loads to the

ballast and guaranteeing the gauge of the line (dis-

tance between the rails). (Magalh

aes, 2007)

However, structural problems arising from use,

which begin with the appearance of cracks, can

progress to the complete breakage of the sleeper. This

rupture causes an overload on the adjacent sleepers,

accelerating the process of structural degradation and

consequently contributing to the appearance of new

cracks. This process can be repeated in such a way

as to result in a sequence of broken sleepers, causing

an increase in the track gauge and compromising the

safety of a speciﬁc section of the railway line, which

can become a considerable risk for the occurrence of

derailment, mainly on curves.

In addition, the position of the sleeper in a “U”

shape on the rails, as seen in Figure 1, ends up in-

https://orcid.org/0000-0002-5862-6271

https://orcid.org/0000-0001-7949-1188

https://orcid.org/0000-0002-5586-2543

creasing the possibility of corrosion of the sleepers

and further increases the cost of maintenance of the

railways. (Magalh

aes, 2007)

Figure 1: Shape of the steel sleeper on the train track

(DNITT, 2003).

Therefore, rail wear and tear can generate ﬁnan-

cial losses and cause risks that directly affect the

safety of train operators. Thus, preventive and correc-

tive maintenance practices are essential to ensure the

reliability of the railroad. However, railway mainte-

nance has a high cost because it depends on qualiﬁed

labor and an extensive and varied table of procedures

to ensure safer and more efﬁcient conditions for the

operation. (Machado et al., 2009).

Therefore an automated system would be more re-

liable, safe, and consistent, increasing the efﬁciency

of railway maintenance and reducing track inspection

time. It would also minimize railway workers’ risks

Gonçalves, T., Bianchi, A. and Yared, G.

Digital Image Processing in the Diagnosis of Cracks in Steel Sheet.

DOI: 10.5220/0011989800003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 1, pages 623-629

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

623

when they are in a dangerous environment. (Malik,

2013)

For example, during maintenance, some sleepers

are replaced depending on the time of use and oth-

ers by the location of cracks, done manually by visual

inspection. Thus, it is important to carry out this ap-

plied research, which aims to produce knowledge and

generate technology to systematize, streamline and

automate (when applicable) the process of diagnos-

ing structural problems in steel sleepers. Contributing

signiﬁcantly to the reduction of operating costs, and

losses arising from accidents, in addition to creating

new business opportunities for the company, consid-

ering that other railroads also have the same needs.

Therefore, this project aims to develop applied re-

search to diagnose structural problems in steel sleep-

ers and identify them between ﬂawless and defective

sleepers. The work will be developed using digital

image processing techniques and pattern recognition

that enable the extraction of information about the ge-

ometry of the sleeper, more speciﬁcally the curvature

of the sleeper edge. A system for diagnosing struc-

tural problems in steel sleepers will be implemented

based on the surface geometry of the collected data,

which will be divided into upper and lower geometry

to compare the model’s effectiveness.

2 LITERATURE REVIEW

The railways emerged during the second Industrial

Revolution (17th and 19th centuries), with the need

to keep up with the progress of the time, bringing

even more economic and social opportunities. In this

context, the railway matrix makes up an important

modal within the transport sector of the world econ-

omy, providing accessibility and mobility for trans-

porting cargo and people. It is important to note that

in addition to logistics, railroads gained prominence

due to some characteristics, among them: capacity of

freight trains, low cost of freight over long distances,

lack of delays due to trafﬁc jams, lower incidence of

thefts and accidents, low-cost energy and great sus-

tainability, since it has low CO2 emissions in the at-

mosphere.

Therefore, in the world’s major economies, rail-

ways represent the basic means of high-density infras-

tructure and highly connected networks in the trans-

port system. For example, according to data obtained

from (ANTT, 2021), railroads represent the main way

of transportation for Russia (81%), Canada (46%),

Australia and the US (43%), and China (37%). That

is, countries with a developed economy have rail lo-

gistics that are very participatory within the transport

sector; this causes mobility to advance the connection

between the main cities in the country and facilitate

the ﬂow of goods.

Given the importance of railroads for the global

economic sector, the improvement of their manage-

ment has been the subject of several studies to auto-

mate and facilitate inspections of railroad components

since it is still a very human-dependent process, mak-

ing it exhaustive and slow. (Rubinsztejn, 2011), for

example, proposed an automatic system based on the

Viola-Jones algorithm for the automatic detection of

the presence or absence of parts of interest on railroad

tracks using real images acquired by a digital camera

installed under a train.

Other innovative works in the area can be cited,

(Rong et al., 2016) use a camera that captures im-

ages of the rails and a vibration sensor and present

a system to detect irregularities on the track and the

wagon wheels through computer vision and analy-

sis of the rail vibration signal (SVD). (Yokoyama

and Matsumoto, 2017) uses an algorithm based on

Adaboost for crack detection in images of concrete

sleepers. It is trained using crack and non-crack char-

acteristics.

(Srinivasan, 2020) uses visual perception and im-

age processing techniques for railway inspection and

anomaly detection. All work is developed in Lab-

View and the images used are extracted through a

webcam, which runs along the entire length of the

railway. Here, edge detection and image convolution,

performed by changing pixels, are sufﬁcient to detect

loose or bent screws and cracks on the sleeper sur-

face. (Franca and Vassallo, 2021) present a method to

inventory and identify the types and defects of sleep-

ers through real images obtained on railways and sub-

ject to various noises. For this, it uses image pro-

cessing, heuristics, and feature fusion, all in an un-

supervised way and through Matlab. Haar transform,

integral imaging, edge detection, entropy calculation,

and topology aspects are applied. Furthermore, (Pas-

sos et al., 2022) use convolutional neural networks

(CNN) to automatically detect defects on the rail sur-

face. In this work, a comparison is made between 10

(ten) CNN models in order to ﬁnd the one that per-

forms better results and accuracy.

The works presented so far have become similar

in that they use image processing techniques to assess

and inspect the conditions of the railways. But un-

like what has been exposed so far, the sleeper’s object

of analysis in this work will be analyzed not by their

surface, but by their curvature. The next topic will de-

scribe in detail the methods used to create a practical

and efﬁcient framework for automatic rail inspection.

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3 METHODOLOGY

Image analysis and its representation play an impor-

tant role in applications related to computer vision

and the construction of visual inspection systems.

This process is characterized as an area where sys-

tems are built to identify, classify, and interpret ob-

jects in a scene. Thus, as it is an iterative software,

which includes a programming language for techni-

cal and scientiﬁc computing and toolboxes, allowing

problem-solving with high versatility (Demuth and

Beale, 2001), Matlab was the software chosen as the

platform for the development of this research.

The ﬂowchart that represents the methodology of

the information control systems used in this work has

5 (ﬁve) main phases and is adapted in the sequen-

tial pattern of (Gonzalez, 2015), which is divided

into: digital image acquisition, digital image pro-

cessing(PDI), and digital image analysis (ADI). The

ﬂowchart represented in Figure 2 describes the steps

of this work, starting from the representation in pixels

and advancing toward the regions and data.

Figure 2: Flowchart of the methodology steps.

3.1 Image Acquisition

Building a database with real images of defective and

non-defective sleepers is challenging, as it depends on

numerous factors related to companies and the protec-

tion of their data and information. Thus, our proof of

concept uses images of sleepers obtained in the labo-

ratory for further testing in the ﬁeld.

The image acquisition process was carried out

through laboratory experiments. All sleepers are

placed exactly in the same place and at the same dis-

tance so that the only variant in the image collection

is the sleeper itself. Thus, the position of the camera

allows for obtaining an image of the sleeper from the

front and above, where it is possible to view the en-

tire edge of the sleeper. Therefore, the database com-

prises a total of 20 (twenty) images, 10 (ten) images

of sleepers with apparent defects, and 10 (ten) images

of sleepers without defects. Figure 3 illustrates one of

the database images.

Therefore, as the images obtained have a lot of

noise, pre-processing becomes essential for a cleaner

image. That is, the manipulations were performed

properly to eliminate useless information or impair

the analysis. We also use techniques to improve

Figure 3: Database image example, faulty sleeper.

lighting, deﬁne the region of interest, and extract at-

tributes.

3.2 Segmentation

In this context, speciﬁcally, image analysis involves

processes based on regions, thus requiring segmenta-

tion and processes based on transforming the image

into the domain of spatial frequencies. Both allow the

extraction of attributes used for pattern recognition,

whether geometric or related to the power spectrum.

Here, segmentation was used with the delimitation of

the object (sleeper) in the region of interest (ROI) to

deﬁne a contour of the two-dimensional geometry of

the edge, the main object of the study.

The main segmentation approaches may involve

linear methods, which act uniformly throughout the

image and are fast and simple but cause loss of in-

formation and image details, as occurs with thresh-

olding methods. Non-linear methods require more

complex implementations to preserve more informa-

tion about image details. The main interpretations in-

volving non-linear segmentation cases are the varia-

tional approach. A technique where an energy func-

tional is deﬁned (cost function) and whose solution

is found when this functional is minimized. Nonlin-

ear partial differential equations are used to represent

the contour, and the contour evolution is expressed as

some function of invariant properties of the image. In

Figure 4 we see the segmented sleeper and the (x, y)

points representing the sleeper segmentation. X de-

termines the column’s position, and y is the line’s po-

sition in the image where the contour is located.

Figure 4: Segmented sleeper and referent chart.

The functional energy minimization problem is

characterized by being ill-conditioned, where differ-

ent solutions can lead to the same minimized func-

tion; i.e. the solution is not unique. There are several

techniques for the solution of the functional. How-

ever, it strongly depends on the type of parameter used

in its deﬁnition. In this context, we will investigate

the segmentation using snakes: active contour. This

Digital Image Processing in the Diagnosis of Cracks in Steel Sheet

625

technique allows us to segment the image from en-

ergy functionals deﬁned by the image. From an initial

curve in the image, it is deformed towards the edge

region. This curve deformation is then accomplished

by minimizing the total functional energy. Therefore,

as it is an effective method for detecting edges of im-

ages with little contrast, presence of noise, and tex-

ture, this technique was used to delimit the edge and

consequently obtain the extraction of points from the

desired contour. (Kass et al., 1988)

3.3 Data Analysis

Thus, once the sleeper edge is detected using snakes,

the delimited edge is transformed into a set of points

x and y, allowing a detailed analysis of the curvature

that forms the sleeper edge.

These data will be pre-processed through point in-

terpolation. Interpolation is a technique used to es-

timate values of functions at intermediate points of

intervals; this determination is made from functions

calculated at the extremes of these intervals. (Knott,

2000).

This technique is necessary so that all points

have the same distance along the axis and conse-

quently form an edge curvature. Even so, the tech-

nique known as spline was chosen for the curve to

be interpolated as smoothly as possible. Consid-

ered an approximation technique, spline interpolation

consists of dividing the interval of interest into sev-

eral subintervals and interpolating these subintervals

with low-degree polynomials, causing the curve to be

smoothed.(Knott, 2000)

Thus, for better analysis and representation of in-

formation about the outline of the sleepers, we sepa-

rated the points that form the outline into upper and

lower parts. We can see this in ﬁgure 5.

Figure 5: Upper and lower contour sleeper points.

After deﬁning the contour points, we calculate

each curve’s curvature (κ) using Fourier Transform

properties proposed by (Estrozi et al., 2003). It is

well accepted that curvature provides an essential rep-

resentation of salient shape points and is invariant

to rigid-body transformations (Estrozi et al., 2003).

Once the one-dimensional signal y was obtained, a

numerical curvature κ was calculated through Equa-

tion 1.

κ =

(1 + y

)

(1)

where y

is the ﬁrst and y

the second derivatives of

one-dimensional signal. After all this process, the

curvature is stored in a matrix to classify the sleeper

as defective or ﬂawless.

The developed algorithm uses the second fre-

quency component of the Fourier transform. Thus, for

this second frequency component, there is a tendency

for the spectrum of the curve extracted from damaged

sleepers to assume higher values when compared to

ﬂawless sleepers. This frequency limit is then deﬁned

from the mean of the ﬂawless sleeper data chosen for

training, which tended to assume the Gaussian param-

eters.

Therefore, the established threshold will deﬁne an

expected pattern in which, above the threshold, it will

consider a defective sleeper and below the threshold a

ﬂawless sleeper and will save this performance. This

whole process will be repeated 110 times, and at the

end, we will have a ranking result based on the av-

erage of this performance. The methodology used to

develop the classiﬁcation system was to test different

combinations between feature extractions, classiﬁers,

and classiﬁer parameters to ﬁnd the attribute sequence

that achieves the best performance for the situation.

3.4 Data Classiﬁcation

To classify the curvature and to separate the defective

sleepers from the healthy ones, the 2nd component

of the Fourier derivative will be used. This approach

was chosen because, in addition to Fourier adequately

analyzing non-periodic functions, there is greater ap-

plicability to problems related to signal processing.

(Junior and Costa, 1996) That is, in the Fourier Trans-

form, the global or semi-local frequency information

is captured along an entire signal or in processing

windows, not oscillating in short intervals.

Thus, according to (Silva et al., 2022) the geome-

try of the sleepers associated with the spatial signs of

the permanent way, have information that helps us to

characterize the railway superstructure. In this sense,

the discrete Fourier transform (DFT), which is a func-

tion composed of samples that can have pixel values

altered along a row or column, is one of the tools that

will help us in digital image processing. It is used

because it is a mathematical technique based on the

decomposition of signals into sinusoids, these sinu-

soids enter a linear system and come out as sinusoids

that can change amplitude and phase, maintaining the

original frequency, thus knowing if the sleeper has a

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fracture or not, let’s visualize the change in amplitude

of this sinusoid.

Thus, Fourier’s second-order derivative method

can be applied to estimate the curvature along the en-

tire isopotential, curves deﬁned by a surface (x and

y), taking the contour of the original shape as one of

its curve levels. In addition, it provides more excel-

lent curvature enhancement, which would contribute

to the greater accuracy of our analysis. (Estrozi et al.,

2003) and (Junior and Costa, 1996)

Finally, after applying the second derivative, a fre-

quency threshold will be established to deﬁne a clas-

siﬁcation region in the image. From this limit, all

frequency components that are below this limit, re-

ﬂecting the amplitude of the curve, will be classiﬁed

as defective sleepers and above as healthy sleepers.

Given the difﬁculty of establishing a threshold, an au-

tomatic global thresholding method was proposed to

efﬁciently establish a threshold and minimize the er-

ror rate. (Gonzalez, 2015)

3.5 Performance Evaluation

Considering the proposed method for defect detec-

tion, its evaluation is based on three known pattern

recognition metrics: Precision, Recall, and F1 Score.

These metrics measure how well the method’s detec-

tion is performed. Precision and recall are metrics

useful for measuring relevance since they show the

amount of data obtained that are relevant (precision),

and the amount of relevant data obtained (recall). The

Equations 2 and 3 describe precision and recall, re-

spectively

Precision =

T P

T P + FP

(2)

Recall =

T P

T P + FN

(3)

where TP is the true positive rate, corresponding to

defect sleepers that were correctly detected, FP, the

false positive rate, a defect sleeper that was detected

incorrectly, and FN, the false negative rate, indicating

no detection when it does.

Using precision and recall, we can calculate the

F1 Score, which represents the balance between the

two metrics, and can be described by Equation 4.

F1 = 2 ∗

Precision ∗ Recall

Precision + Recall

(4)

4 RESULTS

From obtaining the curvature points with the snake,

these are divided between the upper points of the

sleeper and the lower points, but to deﬁne equal and

consistent regions along the contour, we calculate a

midpoint of the sleeper in such a way that values

greater than the midpoint are treated as the top and

lower values as the bottom of the tie.

In the next step, after separating the points, the

data obtained from the sleeper geometry are inter-

polated with the spline function, using a distance of

1 cm. In this case, the interpolation aims to make

evaluating the upper and lower curvatures efﬁcient

and equal. This is because the edge points that form

the curvature of the sleeper that will be analyzed

later, when extracted, are not obtained equally spaced.

Therefore, the interpolation will calculate the internal

points not given, allowing the (approximate) reconsti-

tution of an occupation. In Figures 6 and 8, we can

see the curve before and after interpolating the points.

Figure 6: Lower curve before and after interpolation.

Figure 7: Upper curve before and after interpolation.

The treatment of all these data is part of the re-

sult that will allow us to save these points in a ma-

trix that will be applied to the curvature for its clas-

siﬁcation. Thus, in the algorithm developed for this

work, based on the calculation of the Fourier trans-

form on the curve obtained from each sleeper, the

Digital Image Processing in the Diagnosis of Cracks in Steel Sheet

627

data, but speciﬁcally, the curvature of the sleepers, are

randomly separated, that is, 70% of these curvatures

are separated for training the model and the other 30%

for validation/testing.

In this way, this separation of training and test data

is done 110 times, and each time the code is executed,

the result is saved and the ﬁnal result is an average

performance of these 110 draws. We emphasize that

making this result repeated 110 times was the way

we found so that our result is not biased in a speciﬁc

database, increasing the reliability of the model, con-

sidering that the ﬁnal result is based on an average

of these 110 times the algorithm runs differently. In

addition, the limit automatically deﬁned by our model

was an adjustment based on training data from healthy

sleepers as mean plus standard deviation, assuming

that the data distribution of the chosen parameter is

Gaussian.

Figure 8: DFT of sleeper surface geometry.

In the graph shown in 8 we have the coincidence

of the Fourier transform in the turned curve of each

sleeper. Thus, we can observe, for visual and qualita-

tive purposes, that for this second frequency compo-

nent, there is a tendency for the curve spectrum ex-

tracted from damaged sleepers to assume higher val-

ues when compared to the frequency spectrum recov-

ered from healthy sleepers.

In 1 we have the ﬁnal performance result of our

model. Therefore, we have the overall average hit

rate, the general value of the classiﬁcation of sleep-

ers. We have the average index in the location of de-

fective sleepers and we have the false positives, which

represent healthy sleepers that are being classiﬁed as

damaged. There is a better performance of the points

extracted from the upper part of the sleeper, with

100% in the location of the sleepers; that is, all de-

fective sleepers were detected, contrary to the points

extracted from the lower part of the sleeper, where

81.82 % of defective sleepers were detected. Also,

the false positives at the top are 11.51% as opposed to

the false positives at the bottom.

Therefore, the average performance of the points

Table 1: Results.

Data Precision Defective False Posit

Upper 94,24% 100% 11,51%

Lower 83,03% 81,82% 15,72%

that form the upper curvature of the sleeper edge

has a better global average classiﬁcation performance,

94.24%, than the points that form the lower curvature

of the sleeper edge, 83.03%.

5 CONCLUSIONS

This work proposed applying a pattern recognition

modeling for automatic railway defects investigation

using digital image processing. Its main contribution

is analyzing and classifying sleepers and the conse-

quent rail maintenance management. This innovative

process extracts information from the curvature of the

surface of sleepers, aiming to optimize the mainte-

nance of railways, which is fundamental in preventing

accidents and reducing costs.

The classiﬁcation based on the extraction of the

sleeper surface curvature showed good performance

when we analyzed the upper curvature, the model cor-

rectly classiﬁed 94.24% of the validation data, having

found all the defective sleepers, contrary to the clas-

siﬁcation carried out with the points below, here the

average hit rate for damaged sleepers was 81.82%.

Thus, the system presents a user-friendly and easy-to-

use interface. However, the validation dataset needs

to be bigger, limiting our study, and it is important to

analyze the architecture’s performance in the face of

a greater challenge.

A larger database is proposed for future work,

varying the training parameters. Furthermore, we

suggest that the extraction of the points that form the

curvature of the sleeper be fully automated. In this

work, this was not possible, as the snakes, the method

used, needed help to demarcate points around the ob-

ject of study. We also suggest comparing different

defect detection techniques; these techniques can be

better exploited if we get a bigger image bank.

ACKNOWLEDGEMENTS

The authors thank CAPES, Fapemig, CNPq, and the

Federal University of Ouro Preto for supporting this

work. Also, the authors would like to thank Vale S/A

for enabling the creation of a dataset with real images.

This study was ﬁnanced in part by the

Coordenac¸

ao de Aperfeic¸oamento de Pessoal de

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ıvel Superior - Brazil (CAPES) - Finance Code

001, the Conselho Nacional de Desenvolvimento

Cient

ıﬁco e Tecnol

ogico (CNPQ), Vale S.A. and the

Universidade Federal de Ouro Preto (UFOP).

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