Proc. of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, November 5-8, 2017
Rust Detection using Image Processing via Matlab
Julianne Alyson I. Diaz, Manuel I. Ligeralde Jr., John Anthony C. Jose, Argel A. Bandala
Electronics and Communications Engineering Department
De La Salle University
Manila, Philippines
julianne_diaz@dlsu.edu.ph
Abstract -This research attempted to create a program that
is capable of detecting rust through image processing. Image
processing is known for the manipulation of image through
quantizing the image itself in matrix form. Through this
quantization, it gives opportunity to not only manipulate the
image but also detect a particular subject on the image as well,
such as rust. Through setting the threshold values and the use of
edge detection and segmentation, rusts on the image can be
detected. The threshold values will set the parameters and
characterize what a rust is. The edge detection will check for the
sudden changes of colors in the images. The segmentation will
then determine the colors on the image. The results in the edge
detection and segmentation will be integrated to determine the
rust on the image. The results of the program yield a success rate
90% in detecting rust on images with rusts and did not obtain
any errors on images with no rust.
Keywords—Rust detection; thresholding; edge detection;
segmentation; MATLAB
I.
INTRODUCTION
The most abundant material in present day’s manufactured
equipment, construction, pipes, machinery and etc. are steel
and other alloys, which provides good support and sturdy
structural design. Although steel and other alloys are known
to react to oxygen and moisture due to their iron content,
which forms a red oxide. The chemical composition of a rust
is a compound named iron oxide. For prevention and
maintenance purposes, rust detection is significant, as it is a
sign of material’s deterioration. In the early days, detecting
rust was done manually through a per piece or per section
human inspection. That method is tedious and prone to
human errors due to missed detection during inspection,
which can lead to further damage of the material. With the
presence of technology, detecting rust can become easier and
more accurate due to digitized methods. In this paper, object
detection like in [1-3] will be used in detecting rust. The
process requires segmented images of the material to be
processed in MATLAB for rust detection. Advantages of
using image processing are the accuracy of reading, cost
effective, faster, objective and consistent. The aim of this
study is to implement rust detection through image
processing and yield at least an 80% success rate.
II.
RELATED WORKS
Steel and other alloys are combinations of different
metallic elements that makes a durable material suitable for
construction of different structures and various products,
such as machineries. Although with its iron content it has
become prone to rust when exposed to oxygen. These rusts
now make the material vulnerable to corrosion, in which can
later lead to the degradation of the material. Thus, early
detection of these rust on the surface is important before it
reaches deeper parts of the material and cause further
damage. Unforeseen damages such as these can lead to
machinery failure and structural integrity failure. Thus, this
must be prevented as early as possible. An example of this
incident is the oil spill in CA Chevron oil refinery plant last
2012. The investigation later on tracked the problem rooted
on the corrosion of the pipes, in which could have been
prevented of rust on the pipes were detected earlier [4]. A
cost-effective implementation is by deploying a serpentine
robot on a steel pipe [5].
Image processing is a developing technology with vast
application. One particular application of image processing is
rust detection. Many studies has been done to tackle image
processing based rust detection such as the study done by
Sharma V. & Tejinder T.,the techniques used for rust
detection were discussed. The first step in doing rust
detection using image processing is through obtaining the
data, which is obtaining the image of the object. In this study
the data was obtained automatically through a camera fixed
on the object that is being monitored. The next step proposed
is the detection of rust. In this step it was proposed that
different rust detection techniques should be done, which
might be due to different types and levels of rust. It was
emphasized that different techniques have different steps to
follow. The third step is calculating the area of rust on the
image. This is to determine whether the object is either
partially rusted or totally rusted. An additional feature was
added in their study, which to determine on what maintenance
should be done to the object [6].
In a study conducted by Huwang, N., Son, H., Kim, C., &
Kim, *C, a rust detection program was created to detect rust
and determine the on which area the robot is going to do the
grit-blasting procedure. The first step in their program is the
conversion of the RGB colors to HSI. This procedure was
done to eliminate the probability of false reading. After such,
the image of the rust will then undergo to the process of
classification, to determine what technique or process to be
used in analyzing the rust. The study offered six categories of
techniques. The purpose of these techniques is to classify
whether the pixel belongs to the background or the rusted
area. In these techniques, the neighboring pixels were also
checked for comparison on whether is a rust or part of the
background. Although, this study still needs further testing on
its rust detection part, since its more on a comparative study
978-1-5090-1134-6/17/$31.00
©2017
IEEE
1327
Authorized licensed
use limited to: ULAKBIM UASL
- YILDIZ
TEKNIK UNIVERSITESI.
Downloaded on April 05,2023 at 12:51:40 UTC from IEEE Xplore. Restrictions apply.
Proc. of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, November 5-8, 2017
on the rust detection area and the focus is less on the analysis
of each techniques proposed [7].
In a study conducted by Ghanta, S., Karp, T., & Lee, S.,
which introduces a method for rust detection using wavelet
transform. The algorithm proposed consists of two stages,
training and detection. The program was tested in rust and
non-rust images. Potential areas within the image are
determined using cross-correlation technique. Detection is
mainly based on the fact that rust lack the color of blue thus
the cross correlation is don using a blue plane. Additionally,
the percentage of rust detected within the image is calculated.
The program used classified 33 out of the 55 rust images into
rust images and classified all the non-rust image as non-rust
images. The program was able to detect rust in images but
was limited by the rust size and the image size. The algorithm
used was ineffective in detecting rust sizes of 8x8 blocks and
in images with sizes greater than 256x256 pixels.
Additionally, the program used was only 52% effective in
classifying images with rust [1].
On the image processing developed by Sharifzadeh, M.,
Alirezaee, S., Amirfattahi, R., & Sadri, S., the image
processing was attempted to be used on detecting defects on
steel, such as holes, scratches, coil break and rust. On the
algorithm that the researchers used on the rust detection,
segmentation was done initially. In this section, the
thresholding of the values was done. The thresholding
binarizes the image and uses entropy techniques for
execution. The entropy that was used in this study is the
Shannon’s Entropy. After binarizing, the values of the ones
and zeros were compared the range of values obtained in
thresholding. The success rate that was achieved in this study
was 90.3% on the rust detection algorithm. Although in this
study a different software was used in processing the program
and obtaining sufficient samples has become a problem to the
researchers [8].
To resolve the limitation of image processing base rust
detection, a study was done by Zaidan, B.B. et al., which
introduced a method for rust detection with the use the
concept of texture analysis. The method proposed uses
texture segmentation with the aid of edge detection. The
rough texture of the corrosion areas are detected using the
combination of texture detection and edge detection. The
images are classified as either corroded or not. The method
was effective but application is limited to textured objects.
Furthermore, precision of the method used was not specified
[9].
III.
THEORETICAL AND DESIGN CONSIDERATION
In the design consideration of the image processing to
detect rust, thresholding, edge detection, and segmentation
was done.
Fig 3.1: Thresholding Flowchart
A. Tresholding
Thresholding is the process converting the image into
binary. Thus, it was used to quantize the image. In the
process, the image was set as its input. The program then
assigns binary values to each pixel of the image. The
assigning of binary bits to the matrix of the image is
dependent on the intensity of the background in the image, in
which was needed for edge detection. Although there were
other Thresholding techniques available, such as multi-level
and histogram, they were not utilized due to their inefficiency
in adapting. Adapting in this context, it is the program’s
ability to detect the intensities precisely.
The thresholding used is adaptable to different images and
does not need calibration. In this research, adaptive method
was used, where every pixel were evaluated and correlated
with its surrounding pixels. This is similar to Otsu method
where the value of the binary assigned to the pixel is
dependent on the range of values of rust set and its
surrounding pixels. The advantage of the method used is its
high adaptability to changes in pixel values without losing
precision.
B. Edge Detection
The edge detection on the other hand is the process of
recognizing the frame or boundary of an object in image
processing. A function for edge detection was created in the
study. The output of the Threshold function was set to be the
input of the Edge detection function. Edge detection works
through checking the neighboring pixels if their range of
value is within the acceptable values in their cluster. Given
with that
1328
Authorized licensed use limited to: ULAKBIM UASL - YILDIZ TEKNIK UNIVERSITESI.
Downloaded on April 05,2023 at 12:51:40 UTC from IEEE Xplore. Restrictions apply.
Proc. of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, November 5-8, 2017
Fig 3.1: Edge Detection Flowchart
statement, importance to the adaptability of the threshold
value is important as it is the basis of the edge detection. In
edge detection, the difference in the intensity of light on the
image was used to detect the frame of the object. From that,
the image was clustered depending on the intensity of the
colors in the image. Note that in this process, the program
does not determine what color it is isolating. The purpose of
the function is to prevent errors in the detection of rust
through to the boundaries that it sets.
Edge detection can be done in many ways. Examples are
Prewitt and Sobel operators. Both methods were based on
convolving an image with a filter on both vertical and
horizontal directions. The difference between the two is the
filter convoluted with the image. Both are powerful tools for
edge detection but the precision of the methods decreases in
high-frequency variations in images. The edge detection
function created in this research is based on Shannon entropy
which correlates the image pixel per pixel together with the
adjacent pixels, which offers a much precise analysis without
being affected by high-frequency variation present in the
images used for testing. The novelty of the method is that it
relies on probability. It calculates the probability of a pixel
being an edge pixel based on the pixel surrounding it.
C. Segmentation
Segmentation in image processing is the process of
dividing the image into pieces. The objective of this
segmentation is to
Fig 3.3: Segmentation Flowhchart
classify the image better and determine the areas that are
important of useful in the data that is needed [10]. There are
different types of image segmentation, but in the program
created, color segmentation was done. Color segmentation is
the process of isolating different colors to cluster them. A
function was created that counts the certain number of a
particular color, in this case red, through the matrix of the
image. This function acts as the filtering process of the
program, whose output now determines the final percentage
of rusts in the object.
IV.
ALGORITHM
The rust detection method used is based on image
segmentation and image thresholding. The image are
segregated into red, green, and blue channels. The red
channel image is stored into a 2D matrix. The grayscale of
the image is then acquired. Thresholding is then applied to
the image. The thresholding method used is Shannon entropy
method. From the method, the threshold value is acquired and
is used to binarize the image. After the binarization, the
images are classified into non-rust images and rust images.
For rust images, the black pixel represents the rusted area and
by computing the number of pixels and dividing it to the total
number of pixels, the approximate percentage of rust is
detected.
1329
Authorized licensed use limited to: ULAKBIM UASL - YILDIZ TEKNIK UNIVERSITESI.
Downloaded on April 05,2023 at 12:51:40 UTC from IEEE Xplore. Restrictions apply.
Proc. of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, November 5-8, 2017
Fig 4.1: Rusted Images
Fig 4.1: Block diagram of the rust detection program
V.
SIMULATION AND DISCUSSION OF RESULTS
The program created is tested by inputting 20 image of rust
images and another 20 non-rust image. The images are fed
one by one onto the program which then detects whether it is
non-rust or rust images.
The image was taken using an Iphone camera which has
an 8megapixel resolution. The images were taken randomly
and categorized by the researchers as either rust images or
non-rust images. The part where rust was present within the
image are then cropped and adjusted to a size of 560x560 so
that the image can be processed easier in MATLAB.
A. Rust images testing
The test was done by uploading 20 images of rust into the
program. Shown in Fig 4.1 are the images that were fed to the
program for testing the accuracy of the program.
To test the program’s success rate in detect rust, the
images of rusted surfaces were inputted in the program. There
were three characteristics of the image inputted that the
researchers considered and delimited in the scope of this
study. First, the image taken must be of a metal surface.
Second, the images must show a non-uniform coloring.
Lastly, the discoloration of the image must be yellowish or
reddish. To summarize the three characteristics, the image
data used are only limited to flat metallic surfaces showing
signs of red or yellow rust.
A total of three runs were done to test the accuracy of the
program in detecting rust on images of rust. Shown below is
the actual value of the image where 1 represents a rusted
image and 0 a non-rust image. The first column represents the
numbering of the sample images. The second row represents
the true value of the input image. All true values are 1 for this
testing because all images tested are rust images. The third to
fifth column represents the three runs done to test the images.
Fig 4.2: Non-rust Images
Out of 20 rust images, the program was able to determine
19 as rusted images. From that data, the computed accuracy
of the rust detection program is 90%. Source of error was
encountered when majority of the image is composed of rust.
B. Non-rust images testing
The program is fed with 20 images that does not contain
rust. Shown below are the images that were fed to the
program for testing the accuracy of the program.
The non-rust images taken, are random. To be considered
a non-rust image, the image must fail in meeting at least one
of the three parameters set in considering rust image. The
images taken were not limited to metallic surfaces. To test the
accuracy of the program, non-metallic images were also
included in the sample. The purpose of this testing is to test
the reliability of the program and to make sure that it does not
detect rust on surfaces that does not contain rust.
Similar to the previous testing, a total of three runs were
done to test the accuracy of the program in detecting whether
the inputted images were rust images See Table 4.2. When
the program recognizes rust on the image, the program’s
output states that it is a rust image. Hence, it yields an output
of 1 on the table below. On the other hand, if the program
does not recognize rust on the image, the program’s output
will state that it is a non-rust image. Thus, an output of 0 were
obtained on the table below. In this testing, the true values for
all images tested are 0, since all of the input images are nonrusted images.
Out of 20 rust images, the program was able to classify 20
as non-rusted images. From that data, the computed accuracy
of the rust detection program is 100%.
n = 40
Actual: Non-rust
Actual: Rust
TABLE 4.3: Confusion Matrix
Predicted(Non-rust)
Predicted(Rust)
20
0
2
18
22
18
20
20
1330
Authorized licensed use limited to: ULAKBIM UASL - YILDIZ TEKNIK UNIVERSITESI.
Downloaded on April 05,2023 at 12:51:40 UTC from IEEE Xplore. Restrictions apply.
Proc. of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, November 5-8, 2017
TABLE 4.4: Values Computed
Parameter
Computed value:
Accuracy
Misclassification Rate:
True Positive rate
False Positive rate
Specificity
Prevalence
F1 Score
95%
5%
90%
0%
100%
50%
0.9524
C. Confusion Matrix
A confusion matrix was done to solve for the accuracy,
misclassification rate, true positive rate, false positive rate,
specificity, and the prevalence.
VI.
CONCLUSION
The project aimed to create a rust detection program with
a 90% success rate. To execute the whole program, three
functions were created, namely the thresholding, edge, and
segmentation. These methods set the parameters of a rust and
detects them through their matrix values. The result of the
program yields a 90% success rate in detecting rust on images
and 100% in detecting non-rust images.
Although in this project the researchers encountered errors
in the program when the rust on the image is greater than the
background or that the whole image is rust alone. Since in the
program, the basis whether an object is rusted or not depends
heavily on the color of the image. There were no other inputs
in the program other than the image alone.
In future studies, it is recommended that researchers add
more sample images to further test the accuracy of the
program. Moreover, given the accuracy requirement of the
topic, it is recommended that researchers try other methods
of image processing based rust detection. Lastly, it is
recommended that future works tackle the rust detection
wherein input is a live video feed instead of images.
[4] J. Susong. (2016). Why Should I Care About Carbon Steel
Pipe
Inspection?.
From
http://www.industrialaccess.com/blog/carbon -steel-pipeinspection/, January 26, 2017
[5] A. Bandala, and J. W. F. Orillo. “Development of a
Flexible Serpentine Robot for Disaster Surveillance
Operations,” Jurnal Teknologi. vol. 78, 2016, pp 91-95.
[6] A. Sharma, and T. Tejinder, “Techniques for Detection of
Rusting of Metals using Image Processing: A Survey,”
International Journal of Emerging Science and
Engineering, vol. 1, 2013, pp 60-62.
[7] N. Huwang, H. Son, C. Kim, and *C. Kim, “Rust Surface
Area Determination of Steel Bridge Component for
Robotic Grit-Blast Machine,” In 2013 Proceedings of the
30th ISARC, Montréal, 2013, pp 1148-1156.
[8] M. Sharifzadeh, S. Alirezaee, R. Amirfattahi and S. Sadri,
"Detection of steel defect using the image processing
algorithms," 2008 IEEE International Multitopic
Conference,
Karachi,
2008,
pp.
125-127.
[9] B. B. Zaidan, A. A. Zaidan, H. O. Alanazi, and R.
Alnaqeib,
“Towards
Corrosion
Detection
System,” International Journal of Computer Science
Issues, vol. 7, pp. 33-36, 2010.
[10] A. Taneja, P. Ranjan and A. Ujjlayan, "A performance
study of image segmentation techniques," 2015 4th
International Conference on Reliability, Infocom
Technologies and Optimization (ICRITO) (Trends and
Future Directions), Noida, 2015, pp. 1-6.
REFERENCES:
[1] S. Ghanta, T. Karp, and S. Lee, “Wavelet domain
detection of rust in steel bridge images,” In 2011 IEEE
International Conference on Acoustics, Speech and
Signal
Processing
(ICASSP).
https://doi.org/10.1109/icassp.2011 .5946583
[2] J. P. N. Cruz, M. L. Dimaala, L. G. L. Francisco, E. J. S.
Franco, A. A. Bandala and E. P. Dadios, "Object
recognition and detection by shape and color pattern
recognition utilizing Artificial Neural Networks," 2013
International Conference of Information
and
Communication Technology (ICoICT), Bandung, 2013,
pp. 140-144.
[3] A. Uy, R. Bedruz, A. Quiros, J. Jose, E. Dadios, A.
Bandala, E. Sybingco, and O. Sapang, "Automated
vehicle class and color profiling system based on fuzzy
logic," 5th International Conference on Information and
Communication Technology (ICoIC7), Melaka, 2017, pp.
1-6.
1331
Authorized licensed use limited to: ULAKBIM UASL - YILDIZ TEKNIK UNIVERSITESI.
Downloaded on April 05,2023 at 12:51:40 UTC from IEEE Xplore. Restrictions apply.