@Article{iasc.2020.013922,
AUTHOR = {Gulsah Karaduman, Mehmet Karakose, Ilhan Aydin, Erhan Akin},
TITLE = {Contactless Rail Profile Measurement and Rail Fault Diagnosis Approach Using Featured Pixel Counting},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {3},
PAGES = {455--463},
URL = {http://www.techscience.com/iasc/v26n3/40005},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2020.013922}
}