Open Access
ARTICLE
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
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.
Citations