Railway Turnout Defect Detection Using Image Processing

Document Type : Research Paper


1 Department of Civil Engineering, Amirkabir University of Technology, Tehran, Iran

2 School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran


Despite other modes of transportation, trains move just along one dimension. However, trains inevitably change their track or move to the opposite track in railway stations and ports using switch systems. Switches are vital for better operation and seamless movement of trains. Furthermore, they are crucial for the safety of movement in tracks due to high derailment potentials at switches; therefore, all parts of switches need to be continuously monitored. An increasing number of accidents in railway systems is highly dependent on switch performance. According to the Islamic Republic of Iran Railways, 90 percent of railway accidents in Tehran stations occur on switches, from which 25 percent happen due to switch defects. Therefore, condition evaluation of switches is of significant importance. Research studies have not been sufficiently conducted on automated condition evaluation of switches. This paper aims to develop a robust automated approach to evaluate switch conditions to be able to measure switch defects. Having taken some pictures from various switches with fixed angles and distance from rails, an image processing technique is applied to determine defects. The first step of image processing is to preprocess the images to increase their quality. The second step is to indicate the type and severity of defects using different algorithms. A Graphical User Interface (GUI) is developed to develop a user-friendly tool to be able to load images, preprocess the images, measure defects, and report the health condition of switches. Finally, the outcomes are validated by applying ground truth, which ends up with high accuracy of approximation of 87 percent.


- Aguilar, Juan, Lope, Maria, Torres, Enrique and Blesa, Maria (2005) "Development of a stereo vision system for none-contact railway concrete sleepers measurement based in holographic optical elements", Measurement, Vol. 38, No. 2, pp. 154-165.
- Alippi, Cesare, Casagrande, Ettore, Scotti, Fabio, and Piuri, Vincenzo (2000) "Composite Real-Time Image Processing for Railways Track Profile Measurement", IEEE transaction on instrumentation and measurement, Vol. 49, No. 3, pp. 559-564.
- Baradaran, Vahid (2017) "Assessment and Prioritizing the Risks of Urban Rail Transportation by Using Grey Analytical Hierarchy Process (GAHP)", International Journal of Transportation Engineering, Vol. 4, No. 4, pp. 255-273.
- Benitoa, Mónica, and Peñab, Daniel (2007)"Detecting defects with image data", Computational Statistics & Data Analysis, Vol. 51, No. 12, pp. 6395-6403.
- Canada, Transportation safety board (2009) Statistical summary railway occurrences, communications division. Quebec: Transportation safety board (TSB).
- Clark, Robin (2004) "rail flaw detection: overview and needs for future developments", NDT & E International, Vol. 37, No. 2, pp. 111-118.
- Fernando Molina, Luis, Resendiz, Esther, Edwards, J. Riley, and M. Hart, John (2010) "Condition Monitoring of Railway Turnouts and track components using machine vision." Transportation Research Board (TRB), Washington DC, USA, 23-27.
- Gonzalez, Rafael C, and E Woods, Richard (2010) Digital image processing. New Jersy: Prentice Hall.
- Grossoni, Ilaria, Hughes, Peter, Bezin, Yann, Bevan, Adam, and Jaiswal, Jay (2021) “Observed failures at railway turnouts: Failure analysis, possible causes and links to current and future research.” Engineering Failure Analysis, Vol. 119, No.104987.
- Li, Ying, Trinh, Hoang, Haas, Norman, And Pankanti, Sharath (2014) "rail component detection, optimization, and assessment for automatic rail track inspection", IEEE transactions on intelligent transportation system, Vol. 15, No. 2, pp. 760-770.
- Liu, Ze, Wang, Wei, Zhang, Xiaofei, and Jia, Wei (2011) "Machine vision inspection of railroad track", 2nd International Asia Conference.
- Luis, Camargo Fernando Molina, Edwards, J. Riley, and P. L Barkan, Christopher (2011) "emerging condition monitoring technologies for railway track components and special trackwork", ASME/ASCE/IEEE Joint Rail Conference, Pueblo, Colorado, USA.
- Papaeliass, Mayorkinos, Roberts, Christophe, and Davis, Christina (2008)"A review on non-destructive evaluation of rails: state-of-the-art and future development", rail and rapid transit, Vol. 222, pp. 367-384.
- Shahni Dezfulian, Reza, Jafarpour, Amir, and Mir Mohammad Sadeghi, Seyed Javad (2005) "Investigating the result of setting up a comprehensive Iranian railway network maintenance", 3rd national maintenance conference. Tehran, Iran.
- Sysyn, Mykola, Gerber, Ulf, Nabochenko, Olga, Gruen, Dmitri, and Kluge, Franziska (2019) “Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods” Urban Rail Transit 5, 123–132.
- Singh, Maneesha, Singh, Sameer, Jaiswal, John, and Hempshall, John (2006) "Autonomous Rail Track Inspection using Vision Based System", IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety. Alexandria, Virginia, USA.
- Standards, railroad track. (1991) Army TM 5-628, Air Force AFR 91-44. Washington: departments of the ARMY, and the air force.
- Swely, Kevin, and Reiff, Ray (2000) "An assessment of Railtrack’s methods for managing broken and defective rail", Rail failure assessment for the office of the rail regulator.