Phương pháp phát hiện và đánh giá hư hại gấp nếp cho hệ thống kiểm tra chất lượng đường ray cao tốc

e image of rail track. 
Rail 
Image 
Complete adaptive 
threshold 
Fusion 
image 
Longitudinal defect 
Determine 
Preprocess
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Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 2(65).2019
According to the deviation, Shift the ROI in the 
original image to get the full rail image then 
extract it. 
4. RAIL SURFACE DEFECT DETECTION
Defects are easy to be hidden or confused in rail 
images because of illumination inequality and the 
variation of reflection property of rail surfaces, so 
contrast enhancement is a necessary procedure 
to highlight defects from their background. So 
proposed method aims to identify corrugation 
style of defect on the rail surface image of which 
grey value are larger than adjacent regions in 
longitudinal direction and transversal direction 
respectively. Some of main reasons are showed 
as following:
Fig 4. Gray value Histogram curvature of a rail 
sample image (a) in horizontal (b) and vertical 
direction (c)
- Defect’s features (dimension, formation, grey 
value) always change from image to image and 
challenge the visual inspection systems. Defect 
region maybe very small s < 20 pixels or even 
very large s > 10000 pixels, which spread though 
over the image in longitudinal direction. Thus, if 
evaluate the maximum deviation of grey value in 
longitudinal direction only should be unreliable, 
some very large defect maybe ignored. 
- Generally speaking, the intensity of corrugation 
defects is brighter than that of background. 
However, this order can be changed because of 
variations of illumination and reflection property or 
some small lighter or darker regions.
- In addition, numerous objective noises caused 
of visual inspection systems establishing and 
operating process significantly affect quality and 
performance of acquired images. The noticeable 
ones are time, weather, place of inspection, natural 
light, quality of the image acquisition subsystem 
(IAS), the velocity and the shake of moving test 
train. All of them need to be particularly considered 
for a cancellation in pre-processing.
According to the above analysis, this proposed 
method based on the maximum grey value deviation 
in each column individually then associate with a 
complete adaptive threshold image in the result. 
Detail of the algorithm is showed as follow:
a. Pre-process
Calculate the mean grey value of the whole rail 
image.
Eliminate all the small regions which have too 
large grey value while comparing with the mean 
grey value of whole image.
According to the mean grey value, adjust the 
contrast of the rail image. Then apply boundary 
detecting method by evaluating the Gradient of 
pixels. As definition, Gradient is a vector of which 
components denote the variation velocity of pixels.
(3)
Where dx, dy denotes the distance-by-pixels in x 
and y direction respectively.
Because digital image is discrete signals so 
the partial differential equations are replaced 
by perform a convolution to get the linear 
approximation result. So:
 (4)
In accordance with above analysis, apply the 
method of which advantage to eliminate impact 
of random noises and keep the image detail 
simultaneously:
where x and y are the spatial coordinates of 
acquired raw image f(x,y), g(x,y) represents the 
smoothed image of f(x,y) by a median filter, R(x,y) 
denotes the output image. And α(x,y) is smoothing 
rate parameter which can be derived from the 
following equation. 
 (6)
(a) (c)
(b) Gray value
Gr
ay
 va
lue
( ) ( ) ( )
( ) ( ) ( )
, , ,
, , ,
f x y f x dx y f x y
fx
x dx
f x y f x y dy f x y
fy
y dy
∂ + −
= ≈
∂
∂ + −
= ≈
∂





( ) ( )
( ) ( )
1, ,
, 1 ,
f
fx x y f x y
x
f
fx x y f x y
y
∂
≈ + −
∂
∂
≈ + −
∂





( ) ( ) ( )
( )
, , 1R x y f x yα α= ⋅ + − ( ) ,g x y⋅ (5)
( )
( ) ( ), ,
, / ( )
g x y g x y
a x y max
x x
∂ ∂
=
∂ ∂
10
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Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 2(65).2019
b. Rai Surface Corrugation Defect Identifying
The evaluating and identifying process for 
corrugation defect on the rail surface compose 
these following steps:
- Determine longitudinal defect according to the 
maximum grey value in each column of rail image 
individually.
- Combine with a complete adaptive threshold 
image for eliminating mistaken defects in 
longitudinal consideration.
- Detect and record all the defects in the rail surface 
image for evaluating their influence to the riding 
quality and safety of a railway system in the future.
According to the grey level deviation of the pixels 
in each column of the rain surface image matrix 
to coarsely determine whether corrugation 
defect exist.
Algorithm 2: Rail Surface Corrugation Defect 
Detection
1. Procedure: Defect_Detector (*srcImage, mean)
2. Rows = size(*srcImage.rows)
3. Cols = size(*srcImage.cols)
4. For (i <-1, cols) do
5. MaxCol = 0; minCol = 0; DevCol = 0;
6. For (j <-1, rows) do
7. If maxCol < srcImage(j,i) then
8. MaxCol = srcImage(j,i)
9. End if
10. If minCol > srcImage(j,i) then
11. MinCol = srcImage(j,i)
12. End if
13. ColSum= sum(srcImage(j,i))
14. End for
15. ColAvg = colSum/rows
16. Deviation = maxCol – minCol
17. For (j <-1, rows) do
18. If Deviation > k
1
 * Deviation + k
2
*mean then
19. If srcImage(j,i) > k
3
*(minCol+maxCol + ColAvg) 
then
20. SrcImage(j,i) = maxCol
21. Else srcImage(j,i) = minCol
22. End if
23. Else srcImage(j,i) = minCol
24. End if
25. End for
26. End for
27. End procedure
In algorithm 2, the input parameters contain the 
pre-processed image and its mean grey value. In 
each column of the image, find out the maximum 
and minimum grey value pixels, and calculate 
the mean grey value of the pixels in that column 
simultaneously. If the maximum deviation of the 
pixels in that column larger than a threshold value 
then in that column probably exist defects. 
The contrast of the image before applying 
boundary detect method is adjusted by performing 
a quadratic function y = k4*x2, where k4 = 0.01. 
Combine above two result image using a bit AND 
logical function to eliminate non-defect regions 
on both of them and reshape the derived defects 
simultaneously.
Finding all the large enough defects on the rail 
surface image can be obtained by performing 
a connected component detecting function with 
8-neareast neighbour parameter. In accordance 
with that, exactly identify all the defects and their 
features, thenceforth evaluating the harmful of 
them to rail way operating will be available.
Indicate the defects on the rail surface image 
with a highlighted bounding rectangle, yellow 
for the small ones and red for the serious ones 
respectively. Finally save them to the result 
data set.
5. EXPERIMENTAL RESULTS AND DAMAGE 
LEVEL EVALUATION
We construct an experimental data set which 
consists of 1108 images with and without defect 
in the dimension at 1 m rail track long. The data 
set was supported by ShangHai Suyu Railway 
Material Co. Ltd, collected by a moving test train in 
different illumination and weather conditions. 
Some images from the data set and the results 
derived though applying this propose algorithm 
are shown in Fig. 5
Corrugation defects after extracted are divided 
into two categories according to their size since 
defects with larger size will damage a railway more 
heavily. Generally, a defect with an area larger 
than 314 mm2 is serious and should be detected 
as soon as possible, and a defect with area less 
than 80 mm2 is too tiny to be inspected.
1) Type-I for 80 mm2 ≤ Ad ≤ 314 mm2
2) Type-II for Ad > 314 mm2
As show in Fig 3, the area of a defect is 
approximated by the area of its minimum 
circumscribed rectangle. The Type-I defects are in 
yellow rectangle whereas Typy-II ones are in red 
recctangle. 
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Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 2(65).2019
Our experiment results demonstrate the 
proposed method is robust to noise and it has 
ability to eliminate the illumination inequality of 
rail images, and has ability to identify various of 
defect size and shape.
Total number of image:1108
Number of non-defect image: 552
Precision of rail surface detected: 1096/1108
Precision of defect detected: 542/556
VI. CONCLUSION
The paper has presented a method for detecting 
corrugation defects appearing on high speed rail 
surface mainly based on the histogram gray level 
curvature in both vertical and horizontal direction. 
The results of experiments conducted on 1108 
high-speed ray images shown that the proposed 
method could easily overcome the challenges 
such as ambient light inequality, the vibration of 
the testing-car while moving, the inhomogeneous 
reflection property of the rail surface, defect 
distribution on rail images, ... Since then, the 
proposed method has been demonstraded to 
be robust to noise and efficient performance, 
and consequently be conveniently applied to 
the railway visual inspection system. With the 
successful classification of the level of damage of 
each defect on a specific track, relevant railroad 
period maintenance planning would be made. This 
result also contributes a significant data source 
for the complete railway visual inpection system 
based on deep learning in future work.
REFERENCES
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of railway traffic using fiber Bragg grating 
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[2] C. L. Wei et al., A fiber Bragg grating sensor 
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[3] M. L. Filograno, P. Corredera, and M. 
Rodríguez-Plaza, A. Andrés-Alguacil, and M. 
González-Herráez, Wheel flat detection in 
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4808–4816, Dec. 2013.
[4] Y. Li, H. Trinh, N. Haas, C. Otto, and S. Pankanti, 
Rail component detection, optimization, and 
assessment for automatic rail track inspection, 
IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, 
pp. 760¨C770, Apr. 2014.
Fig 5. Some samples and the results derived though applying our algorithm. (a) Original rail image; 
(b) after pre-processing; (c) longitudinal contrast adjusting; (d) threshold after applying y = x2; 
(e) fusion; (f) connected component; (g) corrugation defect localization
(a) (b) (c) (d) (e) (f) (g) (a) (b) (c) (d) (e) (f) (g) 
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Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 2(65).2019
[5] B. Gao, L. Bai, W. L. Woo, G. Y. Tian, and Y. 
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AUTHORS BIOGRAPHY
Pham Thi Dieu Thuy
- Pham Thi Dieu Thuy received the B.S. degree in automation from Thai Nguyen 
University of Technology, Thai Nguyen, Viet Nam
+ In 2006 and the M.S. degree in Measurement and Control systems from Ha Noi 
University of Science and Technology, Ha Noi, Viet Nam
+ In 2010: Her current research interests include medical image processing, machine 
vision and machine learning
- Email: dieuthuy303@gmail.com
- Telephone No: 0986468005
Ha Minh Tuan
- Ha Minh Tuan received the B.S. degree in automation from Viet Nam Maritime 
University, Hai Phong, Viet Nam
+ In 2005 and the M.S. degree in Measurement and Control systems from Ha Noi 
University of Science and Technology, Ha Noi, Viet Nam
+ In 2010: His current research interests include medical image processing, machine 
vision and machine learning
- Email: minhtuanha031@gmail.com
- Telephone No: 0977536826
Tran Thi Diep
- Tran Thi Diep received the B.S. degree in automation from Thai Nguyen University of 
technology, Thai Nguyen, Viet Nam
+ In 2010 and the M.S. degree in Automation from Mining and Geology University, 
Ha Noi, Viet Nam
+ In 2013: Her current research interests include control systems, nonlinear, robotics, 
renewable energy
- Email: phuongdiep222@gmail.com.vn
- Telephone No: 0374700015 
13
LIÊN NGÀNH ĐIỆN - ĐIỆN TỬ - TỰ ĐỘNG HÓA
Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 2(65).2019
Vu Duc Ha
- Vu Duc Ha received the B.S. degree in Electrical engineering from Hanoi Industrial 
University, Ha Noi, Viet Nam
+ In 2010 and the M.S. degree in Automation from Mining and Geology University, Ha 
Noi, Viet Nam
+ In 2013: His current research interests include control systems, nonlinear, robotics, 
renewable energy
Email: vuhadhsd@gmail.com.vn
Telephone No: 0983954486
Pham Thi Thao
- Pham Thi Thao received the B.S. degree in Electrification and power supply of 
enterprises from Thai Nguyen University of Technology, Thai Nguyen
+ In 2002 and the M.S. degree in Automation from Ha Noi University of Science and 
Technology, Ha Noi, Viet Nam
+ In 2004: Her current research interests include control engineering and automation. 
Summary of current work: Lecturer, Faculty of Electrical Engineering, Sao Do University
- Email: phamhathao@gmail.com
- Telephone No: 0905006188
Luong Thi Thanh Xuan
- Luong Thi Thanh Xuan received the B.S. degree in Technology Education from Thai 
Nguyen University of Technology, Thai Nguyen
+ In 2003 and the M.S. degree in Automation from Thai Nguyen University of Technology, 
Thai Nguyen, Viet Nam
 + In 2011: Her current research interests include Control Engineering and Automation. 
Summary of current work: Lecturer, Faculty of Electrical Engineering, Sao Do University
- Email: thanhxuan7980@gmail.com
- Telephone No: 0982791980