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Abstract— Railways are one of the
most important components of transportation. It is basic need that the railways
has been examine at regular period of time in order to ensure the safety of
railway transportation to prevent the disruption of transportation, and avoid
accidents. To inspect of railway surface by using manual techniques, both
damages and defects of the rail surface and leads to disruption of railway
transport. Railway analysis methods that utilize contactless image processing
techniques are available in the literature in order to avoid these problems. This
paper presents a comparison of rail defect detection methods that are available
in the literature. These methods in the literature have been compared in terms
of feasibility, performance, accuracy, elapsed time, and image processing
techniques used. The pros. and cons. of these methods relative to each other are
examine in this paper.

Keywords—Rail
defects; Image processing techniques; Defect detection; Rail surface analysis,
Crack detection, Wear defect, Rail Failure, etc.

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I.      Introduction

Railway is consider as one of
the safest transportation medium types all over world. The railroad is
spreading rapidly over transportations. As railway has become widespread
throughout world, the importance given to the maintenance and safety of
railways has also increased. Railways are composed of several components. The
most important component is rail. Train accidents happened every year in the
world due to heavy task. And the train accidents resulted in serious
destruction of property and injury or death of passengers and crew members 1.
Many of the railway transport accidents happen because of driver’s tiredness,
bad weather conditions, and defective rail components, etc. To prevent these
accidents, importance is attached to the detection of faulty regions in the
tracks, and other rail components.

Safety of railroad
transportation can be enhanced by utilizing intelligent systems that provide
additional information about the exact location of the train, its speed and
upcoming obstacles. The rails face more and more risk of damage with the
increase of speed 2. Therefore, the rails should be closely inspected for
internal and surface faults. Rail profile analysis using manual methods both
damages the rail surface and temporarily disrupts railway access. For this
reason, rail profile analysis for railway transportation has been done using
contactless image processing techniques. Methods, which detect the rail
failures by means of contactless image processing techniques, are available in
the literature.

Aliza Raza Rizvi, Pervez Rauf
Khan, Dr. Shafeeq Ahmad, 1 Introduce computer vision based method in paper. A
system has been suggested which can periodically take images of the railway
tracks and compared with the existing database of non-faulty track images on a
continuous basis. If a fault arises in the track section, the system will
automatically detect fault and necessary actions can be taken to avoid any
mishappening. Main objective is to find crack in railway track through camera
image and fix it using computer vision based method.

B. Sambo, A. Bevan, C. Pislaru,
2 introduce an intelligent image processing algorithm capable of detecting fatigue
defects from images of the rail surface. The algorithm generates statistical
data (such as total number of detected defects per image, damage index of
entire image, specific region of interest (ROI)). Adaptive histogram equalization
is use for the local contrast enhancement so the defect regions are clearly
visible. Then an adaptive threshold method is employ to segment the defects and
predict crack growth rate and direction. Main objective is to detect fatigue
defects from images of the rail surface. Also, detect crack in railway track
due to shear stresses, hydraulic pressure and fluid entrapment and squeeze film
effect.

Shahrzad Faghih-Roohi, Siamak
Hajizadeh, Alfredo Nunez, Robert Babuska and Bart De Schutter, 3 introduce Deep
Convolutional Neural Networks (DCNNs) for automatic detection of rail surface
defects. Data resembles that of for visual inspection of rails. One immediate
advantage of using a DCNN is that unlike, they do not have to go into an
elaborate procedure for the extraction of features. They can rather use raw
images as input to the classi?cation model, which is subsequently optimized
using a mini-batch gradient descent method for the entire network. They compare
three DCNNs with different structures for their classi?cation accuracy and
computation time. Main objective is deep convolutional neural network solution
to the analysis of image data for the detection of rail surface defects.

S. Sam Jai Kumar, T. Joby
Titus, V. Ganesh, V.S. Sanjana Devi, 4 introduce ultrasonic sensor is used to
detect the crack in the railway track by measuring distance from track to
sensor, if the distance is greater than the assigned value the microcontroller
identifies there is a crack, also it tells the exact location of the crack by
the formula “DISTANCE=SPEED*TIME”. While the checking process is going on, the
train may approach, it is identify by the vibration sensor and gives alert to
the microcontroller. Railways are one of the important transports in India.
There is a need for manual checking to detect the crack on railway track and
always railway personnel takes care of this issue, even though the inspection
made regularly. Sometimes the crack may unnoticed. Because of this the train
accident or derailment may occur. In order to avoid this situation and automate
the railway crack detection has been need to implement.

Gaolong Hu, Ling Xiong,
Jianqiao Tang, 5 introduce the basic idea of mathematical morphology is to
use geometric template (circular, square, rhombus, linear, etc.) of structural
elements with certain shapes to measure and extract the corresponding shape of
the image which in order to achieve the purpose of image analysis and
recognition. this method owns strong anti-noise performance, can detect the
small defect edge accurately under noise, and the peak signal to noise
ratio(PSNR) is 24.SdB in the condition of without reducing the detection speed.
Main objective is to detect heavy rail surface defect due to uneven brightness
and noise.

Orhan Yaman, Mehmet Karakose,
Erhan Akin, 6 introduce An FPGA based method is proposed for rail surface
detection in railway. The propose method is realized by image processing with
FPGA. The image taken on the railway line with the camera attached to the FPGA
development board. Preprocessing is perform on the obtained image. Edge
extraction is apply to the image after pre-processing. The rail surface is
detect using the image obtained because of edge extraction. The propose method
works in real time to monitor and diagnose faults. It detects many defects on
the track surface. The propose method is quite advantageous because of its
real-time operation. Main objective is to check continuously the components
constituting the railway line.

V. R. Vijay Kumar, S.
Sangamithirai, 7 introduce Binary Image Based Rail Extraction (BIBRE)
algorithm to extract the rails from the background. The extracted rails are
enhance to achieve uniform background with the help of direct enhancement
method. The direct enhancement method enhance the image by enhancing the
brightness difference between objects and their backgrounds. The enhanced rail
image uses Gabor filters to identify the defects from the rails. The Gabor
filters maximizes the energy difference between defect and defect less surface.
Thresholding is done based on the energy of the defects. From the thresholder image,
the defects are identified and a message box is generated when there is a
presence of defects. Main objective is to detect the surface defect on railheads.
In order to identify the defects, it is essential to extract the rails from the
background and further enhance the image for thresholding.

Yuvashree G, S. Murugappriya
8 introduce System captures the video of the track from the vehicle that has
camera on the base of the vehicle. This system detects the rail cracks and
misplaced bolts in the tracks. The system the monitoring and structural
condition for railway track using vision based method and calibration to search
the fault location on the track. The percentages of abnormalities are sent to
the maintenance vehicle Driver by hardware unit placed on the driver
cabin.  To prevent such scenario, the
propose system will automatically inspect the rail crack, misplaced bolts and
deadheaded spikes in the railway track. Vision based method camera will be used
to capture the video.

Zehui Mao, Yanhao Zhan, Gang
Tao, Bin Jiang, Xing-Gang Yan, 9 introduce the suspension system states are
augmented with the disturbances treated as new states, leading to an augmented
and singular system with stochastic noises. Using system output measurements,
the observer is designed to generate the needed residual signal for fault
detection. Existence conditions for observer design are analyzed and
illustrated. In term of the residual signal, both fault detection threshold and
fault detectability condition are obtained, to form a systematic detection
algorithm. Simulation results on a realistic vehicle system model is present to
illustrate the observer behavior and fault detection performance. Main objective
is to develop a sensor fault detection scheme with some detection rates for the
suspension system, in which track irregularity is modeled as unknown external
disturbance, and processing and sensor noise are modeled as stochastic zero
mean white noise.

Fig. 1.   
Block
diagram of rail failure detection in the literature 9.

In this paper, we compared
several methods that exist in the literature to each other. It is examined rail
fault detection algorithms, the performance of these algorithms, and the
advantages and disadvantages of these algorithms in relation to each other.
Moreover, these algorithms have been compared in terms of feasibility. Comparative
tables are presented in the following sections.

                                                                                                                                         
II.    Rail
Surface Defects

Failures that occur in the
rails can expressed as wear, scour, breakage, undulation, head check, and
oxidation. Horizontal and vertical abrasions occur on the surface where the
rails are exposed to the wheel. If the amount of wear on the rails is greater
than 33 degrees, railings will be changed or curbing will be done because of
climbing. Rail erosion occurs in horizontal curves, in scissors tongues. Raw
abrasions are divided into vertical and lateral wear. Vertical wear are erosion
in the form of spreading and crushing, which occurs in the rail mushroom of
curves, in the corners of the scissors and on the rail heads in the seals.
Lateral abrasion occurs on the inner cheeks and scissors tongues of the outer
rail under the influence of centrifugal force in the curves 2, 3, and 7.

Headcheck defect is found
around the gauge corner of outer rail and this fault ascending inclines to
happen when cracks reach 30 mm in surface length. The undulation failure can be
expressed that different collapses happen in the rail surface 1, 4. The scour
fault that can happen in the rail is one or several places of the rail due to
the spinning of the locomotive. It should be exchanged rails exceeding the
amount scour 4. Rail oxidation is that crusting, decay, rust and small holes
occur in the rail by effecting humidity, soil and water 1.

Fig. 2.   
An
example of the rail fracture 11.

Fig. 3.   
An
example of headcheck failure that occur in the rail 10.

Fig. 4.   
An
example of undulation defect that occur in the rail 10.

Fig. 5.   
An
example of scour defect that can happen in the rail 10.

Fig. 6.   
An
example of wear defect that can happen in the rail 12.

Fig. 7.   
An
example of oxidation defect that can happen in the rail 13.

                                                                                                                 
III.   The
Rail Defect Detection Methods

Many methods that detect rail surface defects exist in the
literature. These methods employ contactless image processing techniques. So
that the rail surface is not damaged. Besides, possible accidents are prevented
by early detection of many rail failures. Rail fault detection methods that are
frequently used in the literature are presented below.

A.    Rail
Damage Detection using Neural Networks

An onboard measurement system
is for measuring the rail robot’s excursions from the rails midlines and the
rail-robot’s heights above the rail 10. In this method, to deal with the
nonlinearity of the measurement models, the coupling between the outputs, and
the noise contamination, a neural network method is for building high precision
measurement models 10. In addition to different measurement models for
different types of rail tracks are also built based on the proposed neural
network module. Signal processing and neural network module of the method used in
10 appear in Fig. 8.

Fig. 8.   
Signal processing and neural network
module in 1.

B.    Rail
Fault Detection based on the Morphology of Multi-scale and Dual-Structure
Elements

Heavy rail surface defects detected based on the
mathematical morphology of multi-scale and dual-structure elements according to
the characteristics of heavy rail surface defects, uneven brightness and noise
in 5. When this method is compared with the traditional edge detection
operators, the results show that this method owns strong anti-noise
performance, can detect the small defect edge accurately under noise. Using the
morphology of multi-scale and dual-structure elements, defects such as
scratches, rolled-in scale, and uneven rolling on the rails are detected 5.

C.    Rail
Defect Detection using Gabor filters

In the 7, Binary Image Based Rail Extraction (BIBRE)
algorithm is use to extract the rails from the background. The extracted rails
are enhance to achieve uniform background with the help of direct enhancement
method 7. The direct enhancement method enhance the image by enhancing the
brightness difference between objects and their backgrounds 7. The enhanced
rail image uses Gabor filters to identify the defects from the rails. The Gabor
filters maximizes the energy difference between defect and defect less surface.
Thresholding is done based on the energy of the defects. From the thresholder
image, the defects are identify and a message box is generate when there is a
presence of defects 7. The faulty rail image taken as input and the faulty
region detected those are showing in Fig. 9 7.

(a) Input image

(b) Output image

Fig. 9.   
The faulty rail image and detected defects7.

 

 

                                                     
IV.   Comparison
Study of The Rail Defect Detection Methods ?n The Literature

Studies in 1 to 9 have been compared in several
respects. These respects are algorithm’s accuracy rate and operation time, feasibility,
techniques used in fault detection, detectable failures, hardware resource
requirement, used software development environments, and images used in the
algorithm.  The advantages and
disadvantages of these algorithms relative to each other are given in the
following table.

 

TABLE
I.              
A COMPARISON OF THESE METHODS IN LITERATURE

Techniques used in fault detection

Detectable failures

Hardware resource requirement

Used software development environments

Feasibility

Algorithm’s performance criteria

Neural
Networks
Proximity Sensors
 

Detect
nonlinearity of the measurement models (defects)

Hardware
required (Proximity Sensors)

High

High
accuracy rate

Mathematical
morphology of multi-scale
 

Scratch
defect
Backfin
defect
Uneven
rolling
Rolled-in
scale

Hardware
required
(CCD
Camera)

Medium

Strong
anti-noise performance
Peak signal
to noise ratio is 24.5 dB

Binary
Image Based Rail Extraction (BIBRE) algorithm
Gabor
filters
Thresholding
Texture
analysis

Scour
defect

Hardware
required
(Digital camera of 12 megapixels)

Matlab

High

Accuracy
rate 89.9%

Otsu method
Canny edge
detection
Hough
transform
Gauss Filter

Wear defect

Hardware
required
(Special
light sources and laser camera)

Matlab

High

84.3 millisecond
elapsed time
Standard
deviation 1.5
The
approximate speed of the system for 1 frame is 12fps

Morphological
feature extraction
Hough
transform
Edge detection
Laplacian
filter
Gradient
computing

Headcheck defects
Breakage defects
Apletilic defects
Undulation defect

No hardware
required

Matlab

Medium

0.6 sec.
Accuracy
rate 85.3%

Axle box
acceleration (ABA) measurements
Wavelet
power spectrum
Low-pass filtering

Squats
defect

No hardware
required

Medium

Accuracy
rate
78% for
light squats
100% for severe
squats

 

 

 

 

Conclusion

In this paper, we review various methods to detect crack detection
in railway track to avoid accident and mishappening. There are list of methods
can provide better result on their own review but all methods have some
limitation due to uneven action held in railway track. In this paper, we found that
neural network is provide high accuracy rate than other method. It is feasible
to implement method using only sensors. Neural network is better than other
method in terms of feasibility, performance, accuracy, elapsed time and image
processing techniques used.

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