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Multi
Modal System For Safety On Railways

Dheebika.
V, Computer Science and Engineering, S.A. Engineering College

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Lokeswari.R,
Computer Science and Engineering, S.A. Engineering College

Dr
R. Prasanna Kumar, Professor, S.A. Engineering College

Chennai,
India

 

 

 

Abstract

Transportation
networks are one of the most important aspect for economic development of a
country.Accidents in railway leads to
loss of lives and financial loss for the government.Modern railway transport
systems are designed under the principles of safety and reliability, and the
development of high-speed railway lines is based on such premises. This project
is designed based on railway safety. Here we propose a system which consists
of ultrasonic sensor, camera, GPS, and GSM. This project describes a camera
with MATLAB software which is used in
integrating visuals and programs. This also gives a graphical user interface
for this model. This helps us to detect an object  on the track , thus giving us the image of
the hindrance on the track.GPS are
used here to get the location and GSM are used here as a communication channel
to transmit GPS coordinates, like geography location.

 

 

I.INTRODUCTION

 

Railway transportation is known as the
backbone of Indian economy. Safety on railway networks has to be maintained for
the security of the people is guaranteed. Several monitoring system such
as stereo visions, thermal scanners, and vision metric etc., are used in
monitoring platforms. But they could not achieve the goal by detecting the
obstacle on the tracks. The obstacle may be fixed or mobile. Though there is
technological development today, the obstacle detection is done using man
power. This consumes a lot of time, money and man power. The task is also
difficult task for them.

 

The
structure and performance of transportation network reflects the ease of travelling
and transferring goods among the different parts of a country thus affecting
trade and other aspects of country’s economy.

 

 

II.ANALYSIS

 

According
to the statistics, signal system failures, track failures, vehicle breakdown
are some of the cases in which major train accidents occurs. Obstacle on the
train tracks is the most important reason. The obstacle may be any vehicle,
animals and humans crossing the track and also in some cases any cracks on the
rail tracks.  In India, about 60 percent
of the accidents are due to derailments and 33 percent are due to level
crossings.

Around
15,000 lives are lost due to rail accidents every year. The unmanned crossings
are responsible for maximum number of train accidents in India.

 

In all transport systems safety and reliability are
highly considered, particularly in railways. In Railway System all the control
are done through man power. In this present condition we have faced the
following problems wastage of time,
wastage of energy and difficulty for a manual operator. Because of the
constant need to improve rail safety, the existence of  the objects on tracks are considered,
particularly the grade crossings. In the existing system, the chance of false
alarm creation is high. This cause financial loses to the government. 

 

India is the country with one of the largest railway
networks in the world. Safety is one of the major concerns in railways. One the
major accidents zone in our country is by means of railways. Many systems were
proposed but they did not eradicate even a minor part of the accidents in the
country due to many reasons.

 

 

 

LITERATURE SURVEY

 

Juan
Jesús García et al 1 designed a system of barrier for safety enhancement in
railways. This consists of Infrared sensors and ultrasonic sensors. Two
barriers are created, one for emitting purpose and the other for receiving
purpose. The sensors produce signals at frequent intervals. The obstacle is
detected when the signal is created abnormally. Thus the location is detected
using GSM-GPS module.

 

                                             

 

Houssam Salmane et al
2 has proposed a system in which the situations or the events which has
turned out to be abnormal like accidents due to vehicles, pedestrians are
captured through the Level crossing environments. That is the video of level
crossings where the movement is identified and the situation is evaluated
automatically such that if the situation where accident could take place is
detected, a remainder is given. This system works for only a particular zone.

 

Saikat
Ray et al 10 proposed a system a framework for location detection by using
the method of identification of codes. Constructing a framework with minimal
number of sensors is equivalent to creation of optimal code which is of NP complete
problem. This system was created mainly for the use in harsh environments. This
produces an optimal solution for a wide range of parameters. This system does
not need any central monitoring system.

 

 Javier
Hernández-Aceituno et al 8 proposed a method by which the user can know the
definite obstacle that could be in their way in the navigation map used in the
vehicle by prediction metod.

 

Vinh Dinh Nguyen et
al 6 proposed a framework work detecting, recognizing and tracking the
vehicles and the pedestrians by deep learning approach. This system gives
information on robust vehicle and humans crossing the path.

 

IV.PROPOSED SYSTEM

 

In
proposed method we develop a safety system for railway and human beings. This
system or the model provides a way in which accidents due to level crossings
and also in the tracks can be avoided in a greater rate. The
system consist of microcontroller which is interfaced with GPS module, GSM
modem, Buzzer, Ultrasonic Sensor, and LCD display.

The Global Positioning System which is known to be one
of the most successful mobile telecommunication is used such that it gives
total mobility and high transmission rate. The ultrasonic sensors sense the
obstacle in front of the train and send information to the centralized server using
UART and the display unit of the train.

                              

The above diagram shows the working of ultrasonic
sensors where the sound pulse is given out by the sensors at a specific
frequency and they receive the echo. Thus the distance is calculated by the
time elapsed between generated and the received sound wave.  

 

The camera along with the MATLAB capture the detected
obstacle image in front of the train and check what type of obstacles are
detected on the tracks and send the information of the detected obstacle image
to the centralized server using UART. The Universal Asynchronous
Receiver/Transmitter is a microchip with programming that controls the
computer’s interface with the serial devices. 
If any obstacles are detected in front of the train the GPS are used
here to find the location of the obstacles detected train information, and GSM
are used to send the location of the obstacle detected location information to
the nearby railway station by using UART. Here MATLAB are used to
check type of obstacle detected. Buzzer is also used here to produce alarm if
any obstacles are detected in front of the train.

 

                              

 
 
 
 
ARDUINO

Camera

MATLAB

UART

GPS
 

UART

Ultrasonic       Sensor

ADC
 

UART

GSM

        LCD DISPLAY

Buzzer

 

This system gives the video and images of the
obstacles like humans, animals and vehicles crossing the railway line. The
camera helps in capturing image of the obstacle on the train tracks. The system
comes to a conclusion that the detection obstacle is human via face detection
process whereas the animals and the vehicles are detected without providing any
classification. Thus if the obstacles are detected then the system gives an
image or the video through the LCD screen to the driver and also an alarm is
produced by the buzzer.

 

 

ADVANTAGES:

·        
Avoid
accidents at a greater rate

·        
Identify
the obstacles and detects train location

·        
Reduce
time to find the obstacles in front of the train instead of manual involvement
in detecting it.

 

 

 

 

 

 

 

 

 

 

BLOCK
DIAGRAM

           

                       

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

V.
CONCLUSION

Thus
the proposed system helps in eradicating accidents due to humans, vehicles and
vehicles crossing the tracks by using simple mechanism of obstacle detection.

 

 

                                                                  

REFERENCES

1
Juan JesúsGarcía, Álvaro  Hernández, Jesús
Ureña, Enrique García, “FPGA-Based Architecture for a Multisensory
Barrier to Enhance Railway Safety” IEEE
TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 65, NO. 6, JUNE 2016 PP

 

2
HoussamSalmane, LouahdiKhoudour, and YassineRuichek, “A Video-Analysis-Based
Railway–Road Safety System for Detecting Hazard Situations at Level Crossings”
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 16, NO. 2, APRIL
2015 pp 596-609

 

3
Qianru Wang, Bin Guo, Leye Wang, Tong Xin, He Du, Huihui Chen, and Zhiwen Yu
“CrowdWatch: Dynamic Sidewalk Obstacle Detection Using Mobile Crowd Sensing”,
IEEE INTERNET OF THINGS JOURNAL, VOL. 4, NO. 6, DECEMBER 2017  pp 2159-2171

 

4
Nils Gageik, Paul Benz and Sergio Montenegro, “Obstacle detection and collision
avoidance for UAV with complementary low cost sensors”, Received April 14,
2015, accepted May 5, 2015, date of publication May 12, 2015, date of current
version June 1, 2015. Digital Object Identifier 10.1109/ACCESS.2015.2432455pp 599-609

 

5
Néstor Morales, Jonay Toledo, Leopoldo Acosta, Javier Sánchez-Medina, “A
Combined Voxel and Particle Filter-Based Approach for Fast Obstacle Detection
and Tracking in Automotive Applications”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION
SYSTEMS, VOL. 18, NO. 7, JULY 2017, pp 1824-1834

 

6
VinhDinh Nguyen, Hau Van Nguyen, DinhThi Tran, Sang Jun Lee, and Jae WookJeon,
“Learning Framework for Robust Obstacle Detection, Recognition, and Tracking”,
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 18, NO. 6, JUNE
2017, pp 1633-1646

 

7
MatejKristan, VildanaSuli´cKenk, StanislavKova?ci?, JanezPerš, “Fast
Image-Based Obstacle Detection From Unmanned Surface Vehicles” IEEE
TRANSACTIONS ON CYBERNETICS, VOL. 46, NO. 3, MARCH 2016 pp 641-654

 

8
Javier Hernández-Aceituno, Rafael Arnay, Jonay Toledo, and Leopoldo Acosta,
“Using Kinect on an Autonomous Vehicle for Outdoors Obstacle Detection”, IEEE
SENSORS JOURNAL, VOL. 16, NO. 10, MAY 15, 2016, 
pp. 3603-3610

 

9
K. Guan, Z. Zhong, and B. Ai, “Assessment of LTE-R using high speed railway
channel model,” in IEEE Proc. 3rd Int. Conf. Communications Mobile Computing,
2011, pp. 461–464

 

10
Saikat Ray, David Starobinski, Ari Trachtenberg, and RachaneeUngrangsi, “Robust
Location Detection With Sensor Networks”, IEEE JOURNAL ON SELECTED AREAS IN
COMMUNICATIONS, VOL. 22, NO. 6, AUGUST 2004, pp 1016-1025

 

11
H.-R. Trankler and O. Kanoun, “Improvement of sensory information using
multi-sensor and model-based sensor systems,” in Proc. IEEE Instrum. Meas.
Technol. Conf. (IMTC), vol. 3. May 2005, pp. 2259–2263

 

12
K. Chakrabarty, S. S. Iyengar, H. Qi, and E. Cho, “Grid coverage for
surveillance and target location in distributed sensor networks,” IEEETrans.
Comput., vol. 51, pp. 1448–1453, Dec. 2002.

13
S. Meguerdichian, S. Slijepcevic, V. Karayan, and M. Potkonjak, “Localized
algorithms in wireless ad-hoc networks: Location discovery and sensor
exposure,” in Proc. ACM MOBICOM, Long Beach, CA, Oct. 2001, pp. 106–116.

 

14
K. Celik, S.-J.Chung, M. Clausman, and A. K. Somani, “Monocular vision SLAM
for indoor aerial vehicles,” in Proc. IEEE/RSJ Int. Conf. Intell.Robots
Syst., Oct. 2009, pp. 1566_1573.

 

15
M. C. Kang, S. H. Chae, J. Y. Sun, J. W. Yoo, and S. J. Ko, “A novel
obstacle detection method based on deformable grid for the visually
impaired,” Consumer Electronics, IEEE Transactions on, vol. 61, pp.
376-383, 2015

 

16
A. D. Zayas, C. A. G. Perez, and P. M. Gomez, “Third-generation partnership
project standards: For delivery of critical communications for railways,” IEEE
Veh. Technol. Mag., vol. 9, no. 2, pp. 58–68, 2014

 

17
A. Ess, B. Leibe, K. Schindler, and L. van Gool, “A mobile vision system for
robust multi-person tracking,” in Proc. IEEE Conf. Comput.Vis. Pattern
Recognit. (CVPR), Jun. 2008, pp. 1–8.

 

18
Subhan Khan, MujtabaHussainJaffery, AtharHanif, and Muhammad RizwanAsif,
“Teaching Tool for a Control Systems Laboratory Using a Quadrotor as a Plant in
MATLAB” IEEE TRANSACTIONS ON EDUCATION, VOL. 60, NO. 4, NOVEMBER 2017, pp
249-256

 

19
J. L. Guzman, S. Dormido, and M. Berenguel, “Interactivity in education: An
experience in the automatic control field,” Comput. Appl. Eng.Educ.,
vol. 21, no. 2, pp. 360–371, 2013.

 

20
P. Bahl and V. N. Padmanabhan, “RADAR: An in-building RF-based user location
and tracking system,” in Proc. IEEE INFOCOM, Tel-Aviv, Israel, Mar.
2000, pp. 775–784.

 

21
A. Sniady and J. Soler, “Impact of the traffic load on performance of an
alternative LTE railway communication network,” in Proc. IEEE 13th Int. Conf.
ITS Telecommunications, 2013, pp. 396–401

 

22J.
JesúsGarcía, Manuel Mazo, Ana Jiménez, “Efficient Multisensory Barrier for
Obstacle

Detection
on Railways” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 11,
NO. 3, SEPTEMBER 2010. Pp 702-713

23 Dhiraj Sinha, Farhan Feroz, “Obstacle Detection on
Railway Tracks Using Vibration Sensors and Signal Filtering Using Bayesian
Analysis” IEEE SENSORS JOURNAL, VOL. 16, NO. 3,

FEBRUARY
1, 2016 pp. 642-649

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