Mobile Application Detection of Road Damage using Canny Algorithm

Journal of Physics: Conference Series
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Mobile Application Detection of Road Damage using Canny Algorithm
To cite this article: G Gunawan et al 2018 J. Phys.: Conf. Ser. 1019 012035
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1st International Conference on Green and Sustainable Computing (ICoGeS) 2017 IOP Publishing
IOP Conf. Series: Journal of Physics: Conf. Series 1019 (2018) 012035 doi:10.1088/1742-6596/1019/1/012035
Mobile Application Detection of Road Damage using Canny
Algorithm
G Gunawan1
, Heri Nuriyanto2
, S Sriadhi3
, Achmad Fauzi4
, Ari Usman5
, F
Fadlina6
, Haida Dafitri5
, Janner Simarmata7
, Andysah Putera Utama
Siahaan8,9
and Robbi Rahim8*
1
Department of Computer Engineering and Informatics, Politeknik Negeri Medan,
Indonesia
2
Department of Informatics, STMIK Dharma Wacana, Indonesia
3
Department of Electrical Engineering, Universitas Negeri Medan, Indonesia
4
Department of Computer Engineering, STMIK Kaputama, Indonesia
5
Department of Informatics, Universitas Harapan Medan, Indonesia
6
Department of Informatics Management, AMIK STIEKOM Sumatera Utara,
Indonesia
7
Universitas Negeri Medan, Indonesia
8
School of Computer and Communication Engineering, Universiti Malaysia Perlis,
Malaysia
9
Faculty of Computer Science, Universitas Pembangunan Panca Budi, Indonesia
*usurobbi85@zoho.com
Abstract. Edge detection is one of the most frequent processes in digital image processing for
various purposes, one of which is detecting road damage based on crack paths that can be
checked using a Canny algorithm. This paper proposed a mobile application to detect cracks in
the road and with customized threshold function in the requests to produce useful and accurate
edge detection. The experimental results show that the use of threshold function in a canny
algorithm can detect better damage in the road.
1. Introduction
One of the image processing techniques used is edge detection [1]. Edge detection is standard in
digital image processing because it is one of the first steps in segmenting the image, which aims to
present the objects contained in the picture [1] [2] [3] [4]. Edge detection serves to identify the
boundary of an object over an overlapping image. So that if the edge line in the picture could
accurately identify, all objects could found and the fundamental properties such as area, shape, and
size of the object can be measured [5] [6]. Image edge is the position where the pixel intensity of the
picture changes from low value to high value or vice versa [1] [5]. Currently, several algorithms could
use for edge detection, for example, Sobel [7], Canny [7], Prewitt [7], frei-chen [7] and Laplacian
algorithms [8]. In this research taken canny algorithm, Edge is a change of intensity value of gray
degree quickly or suddenly in a short distance [5] [8]. The purpose of detecting the edges themselves
is to group the objects in the image, and also be used to analyze the picture further [9] [10] [11] [12].

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In addition to the use of canny algorithm, this research also uses threshold function in application
which is a parameter to determine the level of image clarity of edge detection results done, the crack
examination on the road is very important to detect the condition of the road that is used because of
the effect on the safety of the street drivers, fracture on roads could be taken seriously by repairing the
road.
Chitra [4] applies the gradient calculation of magnitude and gradient of direction to improve edge
detection in the image, then Biswas [13] enhances edge detection by applying fuzzy sets to reduce
non-noise in the picture so that the accuracy level is higher, in principle improvisation edge detection
canny algorithm is possible done, only by using the threshold function applied to the mobile
application contained in the experimental results in this paper.
2. Methodology
The canny algorithm is an algorithm that performs the examination with the minimum error rate to
produce the optimum edge image, but the canny algorithm process is not sufficient to reduce the
picture that has many edges or strokes which is really an edge or not, for that canny algorithm in
Improvised by adding a threshold function so that edge detection errors on the image can be reduced
well by providing the minimum and maximum values at the time of edge detection processing.
Results of this research was conducted to detect cracks in the road which is the beginning of
damage to the road, to facilitate the process of detecting road cracks directly designed a mobile-based
applications to detect road damage with processors using canny algorithms and threshold functions,
with applications designed policy makers can take a direct solution to fix the road or just do
maintenance only.
The selection of canny algorithm as edge detection process because ease of implementation in the
language of various programming language especially Java besides also easy to be developed by
applying various function especially threshold function.
There are some of the most optimum edge detection criteria that can be met by canny algorithm [3]
[10] [11]:
1. Detect well, Ability to put and mark all existing edges under the selection of convolution
parameters performed. It also provides very high flexibility regarding determining the level of
edge thickness detection as desired.
2. Localize well with Canny; it is possible to produce a minimum distance between the edges
detected with the original edge.
3. The response is clear, there is only one response to each edge so easily detected and does not
cause confusion on subsequent image processing.
There is five step in performing canny edge detection [5] [7] [14]:
1. Eliminate the noise present in the picture by implementing the Gaussian filter. This process will
produce an image that looks a bit blurry, and this is intended to get the edge of the actual picture.
If not performed then fine lines will also be detected as an edge. Here is one example of a
Gaussian filter with σ = 1.4.
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2. Perform edge detection by searching horizontally (Gx) and vertically (Gy).
3. Determining the edge direction found using the following formula.
Gx
Gy
arctan=θ

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Next, divide into four colors so that the lines with different directions and have different colors.
The division is 0 - 22.5 and 157.5 - 180 degrees yellow, 22.5 - 67.5 are green, and degrees 67.5 -
157.5 are red
4. Minimizes the edge line that appears by applying non-maximum suppression to produce a slimmer
edge line.
5. The final step is binary by applying two thresholds, the following figure will show the image
shape before processing (a) and after processing (b). The image used is a grayscale image with
threshold value 0.05.
Thresholding is used to adjust the amount of gray degree present in the picture. By using
Thresholding the gray degree can be changed as desired, e.g. desired using the level of gray 16, then
live to divide the value of a gray degree with 16. The process of Thresholding is a process of changing
the quantization of the image, so to do Thresholding with the gray degree can be used the formula:
Where:
W is the gray degree value before Thresholding
B is the desired number of degrees of gray
X is the gray degree value after Thresholding
Figure 1. Before and After Edge Detection
The results of this research is a smartphone application that can be used to detect cracks in roads with
canny algorithms and threshold functions, apps designed using Android Studio and Android API 24
SDK
3. Result and Discussion
The Canny algorithm is one of the operators used for edge detection, and this operator is like a Sobel
operator. Convolution canny algorithm involving canny kernel and convolution is perform with pixel
value of the image that to be processed, figure 2 below will be test for edge detection.

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Figure 2. Crack damage in the road
For the process of detecting cracks in the road could be seen in the following pseudo code, the code is:
public void setLowThreshold(float threshold) {
if (threshold < 0) throw new IllegalArgumentException(); lowThreshold = threshold;
}
public float getHighThreshold() {return highThreshold;}
public void setHighThreshold(float threshold) {
if (threshold < 0) throw new IllegalArgumentException();
highThreshold = threshold; }
Arrays.fill(data, 0);
int offset = 0;
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
if (data[offset] == 0 && magnitude[offset] >= high) {
follow(x, y, offset, low);
}
offset++;
} }
private void follow(int x1, int y1, int i1, int threshold) {
int x0 = x1 == 0 - x1 : x1 - 1;
int x2 = x1 == width – 1 - x1 : x1 + 1;
int y0 = y1 == 0 - y1 : y1 - 1;
int y2 = y1 == height - 1 - y1 : y1 + 1;
data[i1] = magnitude[i1];
for (int x = x0; x <= x2; x++) {
for (int y = y0; y <= y2; y++) {
int i2 = x + y * width;
if ((y != y1 || x != x1)
&& data[i2] == 0
&& magnitude[i2] >= threshold) {
follow(x, y, i2, threshold);
return;} } }
}

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The above pseudo code are some critical processes for detecting crack paths with canny algorithms
and threshold functions. The result of edge detection process from the figure 2 by using the canny
algorithm could be seen in figure 3 below:
Figure 3. Edge Detection Result
Based on the process of the canny algorithm that has been described and implement in mobile based
edge detection application, the results can see in figure 4 below:
Figure 4. Edge Detection Result in Mobile Application
In Figure 4 there are some results of the edge detection process with the same object but have different
results when the threshold value is replaced, so it can be concluded that the edge detection results in
the image with the canny algorithm can affect when the threshold value on the picture is change, and
this process also affects the time of edge detection process.
The Result of detection road damage by using canny algorithm and threshold function produce fast
enough when using application because every object of image changed into grayscale image, and then
by determining the minimum and maximum threshold values can reduce the detection error against
objects that are not part of the road damage.
4. Conclusion
The Canny algorithm applied to a mobile application can be used to detect damage on the road very
well with accurate results, but also by adding threshold function to the image can know the cracks in

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the image more precisely because it will eliminate the smaller edge of pixel intensity of the edge pixel
image that had crack.
5. References
[1] D. Apdilah, M. Y. Simargolang and R. Rahim, "A Study of Frei-Chen Approach for Edge
Detection," International Journal of Scientific Research in Science, Engineering and Technology,
vol. 3, no. 1, pp. 59-62, 2017.
[2] R. Rahim, T. Afriliansyah, H. Winata, D. Nofriansyah, Ratnadewi, and S. Aryza, “Research of
Face Recognition with Fisher Linear Discriminant,” IOP Conf. Ser. Mater. Sci. Eng., vol. 300, p.
012037, 2018.
[3] H. Banouni, B. Faiz, D. I. E. H. Ouacha, M. Boutaib and M. Derra, "The Canny Edge Detection
Method vs. the Radius of Curvature Method for Determining the Time of Flight on Ultrasound,"
International Journal of Signal System Control and Engineering Application, vol. 9, no. 3, pp. 48-
54, 2016.
[4] A. D. Chitra and P. Ponmuthuramalingam, "An Approach for Canny Edge Detection Algorithm for
Face Recognition," International Journal of Science and Research (IJSR), vol. 4, no. 11, pp. 24-
27, 2015.
[5] S. Vijayarani and M. Vinupriya, "Performance Analysis of Canny and Sobel Edge Detection
Algorithms in Image Mining," International Journal of Innovative Research in Computer and
Communication Engineering, vol. 1, no. 8, pp. 1760-1767, 2013.
[6] V. Saini and R. Garg, "A Comparative Analysis on Edge Detection Techniques Used in Image
Processing," IOSR Journal of Electronics and Communication Engineering (IOSRJECE), vol. 1,
no. 2, pp. 56-59, 2012.
[7] S. Bhardwaj and A. Mittal, "A Survey on Various Edge Detector Techniques," Procedia
Technology, vol. 4, pp. 220-226, 2012.
[8] R. Muthukrishnan and M. Radha, "EDGE DETECTION TECHNIQUES FOR IMAGE
SEGMENTATION," International Journal of Computer Science & Information Technology
(IJCSIT), vol. 3, no. 6, pp. 259-265, 2011.
[9] Y. Ramadevi, T. Sridevi, B. Poornima and B. Kalyani, "SEGMENTATION AND OBJECT
RECOGNITION USING EDGE DETECTION TECHNIQUES," International Journal of
Computer Science & Information Technology (IJCSIT), vol. 2, no. 6, pp. 153-161, 2010.
[10] A. Abbaszadeh and S. Daneshvar, "A Review Of Different Type," JOURNAL OF CURRENT
RESEARCH IN SCIENCE, vol. 1, pp. 871-875, 2016.
[11] N. Ahmadi and G. Akbarizadeh, "Iris Recognition System based on Canny and LoG Edge
Detection Methods," Journal of Soft Computing and Decision Support Systems, vol. 2, no. 4, pp.
26-30, 2015.
[12] D. Nofriansyah et al., “A New Image Encryption Technique Combining Hill Cipher Method,
Morse Code and Least Significant Bit Algorithm,” J. Phys. Conf. Ser., vol. 954, no. 1, p. 012003,
2018.
[13] R. Biswas and J. Sil, "An Improved Canny Edge Detection Algorithm Based on Type-2 Fuzzy
Sets," in 2nd International Conference on Computer, Communication, Control and Information
Technology, India, 2012.
[14] G. Yang and F. Xu, "Research and analysis of Image edge detection algorithm Based on the
MATLAB," Procedia Engineering, vol. 15, pp. 1313-1318, 2011.

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