Image Processing Based Level Crossing Detection and Foreign Objects Recognition Approach in Railways

Year 2017, Special Issue (2017), 19 - 23, 24.09.2017

Canan Tastimur , Mehmet Karakose Erhan Akin

https://doi.org/10.18100/ijamec.2017SpecialIssue30465

Cited By: 19

Abstract

Level crossings are an important part of rail and road transportation and are areas where serious accidents occur in. Most of the accidents in railway transportation are happening in the level crossings. In this paper, a vision-based method is proposed for the prevention of these accidents in the level crossing. With this method, which is based on image processing, the condition monitoring of the level crossing is performed. The obstacles in the level crossing are detected and the estimated distance of these obstacles to the camera is calculated in the proposed method. In order to detect the obstacles in the level crossing, the level crossing in the railway image is determined first. YCbCr color transformation, edge extraction, filtering and Hough transformation have been applied to the image in the detection of the level crossing. The detected level crossing area has been labeled as the grade crossing in the image. It has been checked whether or not it has obstacles at the level crossing. HSV color transformation, image difference extraction, gradient calculation, filtering, detection of connected components and feature extraction have been applied to object detection. A single camera has been used in the proposed method to calculate the distance between the detected foreign object and the camera. The number of pixels covered by the object in the image is taken into account in calculating the distance between the object and the camera. The distance of objects at different distances from the camera is calculated in proportion to the number of pixels in the reference image. This study provides an improvement in this area due to the fact that studies on the literature related to the determination of the level crossing and foreign objects in the level crossing based image processing are not enough. 

Keywords

Level crossing, Hough transform, Feature extraction, Image processing, Foreign object, Distance measurement

References

• S. K. Dwarakanath, S. B. Sanjay, G. B. Soumya, V. Arjun, R. Vivek, “Arduino Based Automatic Railway Gate Control and Obstacle Detection System”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 5, Issue 5, May 2016.
• Z. Silar and M. Dobrovolny, “Objects Detection and Tracking on the Level Crossing”, Computational Collective Intelligence. Springer International Publishing, pp. 245-255, 2015.
• V. Amaral, F. Marques, A. Lourenço, J. Barata, and P. Santana, “Laser-Based Obstacle Detection at Railway Level Crossings”, Journal of Sensors, Vol: 2016, 2016.
• Z.W. Kim and T. E. Cohn, “Pseudoreal-Time Activity Detection for Railroad Grade-Crossing Safety”, IEEE Transactions on Intelligent Transportation Systems, Vol. 5, No. 4, December 2004.
• J. Heavisides, J. Barker and M. Woods, “Hot topics in controlling risk at level crossings”, Arthur D. Little, UK and 2Rail Safety & Standards Board, UK, 2006.
• Y.R. Pu, L.W Chen, and S. H. Lee, “Study of moving obstacle detection at railway crossing by machine vision”, Y.-R. Pu. Informational Technology Journal, 13.16 pp: 2611-2618, 2014.
• Rail System, (2014), “Railway Level Crossing Transition: Risk Measuring Model”. [Online]. Available: http://www.railsistem.com/demiryolu-hemzemin-gecitleri-risk-olcum-modeli/
• Y. Yilmaz, “The Railway Accidents on the Level Crossings in Turkey and suggestions and Regulations to Reduce Them”, M.Sc. Thesis, Gazi University Institute of Science and Technology, 2013.
• Century Group, (2017), “Lagless Crossings”. [Online]. Available: http://www.centurygrp.com/Products/Railroad-Grade-crossings/Lagless-Crossings
• S.L. Lee and C. C. Tseng, “Image sharpening using matrix Riesz fractional order differentiator and discrete sine transform”, 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), doı: 10.1109/ICCE-TW.2016.7520915, pp. 1-2, 2016.
• University of Tartu, (2014), “Digital Image Processing”. [Online]. Available: https://sisu.ut.ee/imageprocessing/book/5
• MathWorks, (2017), “Convert from YCbCr to RGB Color Space”. [Online].Available: ttps://www.mathworks.com/help/images/convert-from-ycbcr-to-rgb-color-space.html?requestedDomain=www.mathworks.com
• Roman10 A Journey to Software Craftsmanship, (2011), “YCbCr Color Space–An Intro and its Applications”. [Online]. Available: http://www.roman10.net/2011/08/18/ycbcr-color-spacean-intro-and-its-applications/
• F. Z. Chelali, N. Cherabit, and A. Djeradi, Face recognition system using skin detection in RGB and YCbCr color space, 2015 2nd World Symposium on Web Applications and Networking (WSWAN), DOI: 10.1109/WSWAN.2015.7210329, pp: 1-7, 2015.
• E. Onat, “FPGA implementation of real time video signal processing using Sobel, Robert, Prewitt and Laplacian filters”, 2017 25th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, 2017.
• P. M.L. Nguyen, J.H. Cho, S.B. Cho, “An architecture for real-time hardware co-simulation of edge detection in image processing using Prewitt edge operator”, 2014 International Conference on Electronics, Information and Communications (ICEIC), pp. 1-2, 2014.
• A. Jose, K. D. M. Dixon, N. Joseph, E. S. George, V. Anjitha, “Performance study of edge detection operators”, 2014 International Conference on Embedded Systems (ICES), pp: 7-11, 2014.
• X. Bai, M. Liu, Z. Chen, P. Wang, Y. Zhang, “Multi-Focus Image Fusion Through Gradient- Based Decision Map Construction and Mathematical Morphology”, IEEE Access, Vol. 4, pp. 4749 - 4760, DOI: 10.1109/ACCESS.2016.2604480, 2016.
• L. Huang, W. Zhao, B. Abidi, M. Abidi, “A Constrained Optimization Approach for Image Gradient Enhancement”, IEEE Transactions on Circuits and Systems for Video Technology, Volume: PP, Issue: 99, 2017.
• Z. Ni, L. Ma, H. Zeng, C. Cai, K.K. Ma, “Gradient Direction for Screen Content Image Quality Assessment”, IEEE Signal Processing Letters, Volume: 23, Issue: 10, pp. 1394 - 1398, DOI: 10.1109/LSP.2016.2599294, 2016.
• T.W. Su, J.Y. Liu, Y.H. Yang, “Weakly-supervised audio event detection using event-specific Gaussian filters and fully convolutional networks”, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 791 - 795, DOI: 10.1109/ICASSP.2017.7952264, 2017.
• C. Taştimur, M. Karaköse, and E. Akın, “A Vision Based Condition Monitoring Approach for Rail Switch and Level Crossing using Hierarchical SVM in Railways”, International Journal of Applied Mathematics, Electronics and Computers (IJAMEC), Vol: 4-Special Issue, pp: 319-325, 2016.
• C. Taştimur, M. Karaköse, and E. Akın, “Detection of Foreign Objects in Railway Level Crossings Using Image Processing Techniques”, International Conference on Advanced Technology & Sciences ( ICAT’17), pp: 68-73, 2017.
• M Karakose, O Yaman, M Baygin, K Murat, E Akin, “A New Computer Vision Based Method for Rail Track Detection and Fault Diagnosis in Railways”, International Journal of Mechanical Engineering and Robotics Research (IJMERR), Vol. 6, No. 1, pp. 22-27, 2017.
• C. Taştimur, M. Karaköse, and E. Akın, “A Vision Based Detection Approach for Level Crossing and Switch in Railway", International Conference on Advanced Technology & Sciences, ICAT’16, pp: 217-223, 2016.