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Contactless Rail Profile Measurement and Rail Fault Diagnosis Approach Using Featured Pixel Counting

Gulsah Karaduman*, Mehmet Karakose, Ilhan Aydin, Erhan Akin
Firat University, Computer Engineering Department, 23100, Elazig, Turkey.
* Corresponding Author: Gulsah Karaduman,

Intelligent Automation & Soft Computing 2020, 26(3), 455-463. https://doi.org/10.32604/iasc.2020.013922

Abstract

The use of railways has continually increased with high-speed trains. The increased speed and usage wear on the rails poses a serious problem. In recent years, to detect wear and cracks in the rails, image-based detection methods have been developed. In this paper, wears on the surface of railheads are detected by contactless image processing and image analysis techniques. The shadow removal algorithm with a minimal entropy method is implemented onto the noise-free images to eliminate the light variations that can occur on the rail. The Hough transform is applied on the noise and shadow free image in order to determine the rail edge and the KNN nearest neighbour algorithm is applied the image to detect the surface of the railhead at the same time. Both of these methods result in new images that are combined. Therefore, minimum errors are seen in detection of rail wear using this method.

Keywords

Rail profile, Hough transform, shadow removal, KNN.

Cite This Article

G. Karaduman, M. Karakose, I. Aydin and E. Akin, "Contactless rail profile measurement and rail fault diagnosis approach using featured pixel counting," Intelligent Automation & Soft Computing, vol. 26, no.3, pp. 455–463, 2020.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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