  Application of electronic railway systems as well as the implication of
Automatic Train Control (ATC) System has increased the safety of rail
transportation. However, one of the most important causes of accidents on the
railway is rail damage and breakage. In this paper, we have proposed a method
that the rail region is first recognized from the observation area, then by
investigating the image texture processing data, the types of rail defects
including cracks, wear, peeling, disintegration, and breakage are detected. In
order to reduce the computational cost, the image is changed from the RGB color
spectrum to the gray spectrum. Image texture processing data is obtained by the
two-dimensional Gray Levels Co-occurrence Matrix (GLCM) at different angles;
this data demonstrates second-order features of the images. Large data of
features has a negative effect on the overall accuracy of the classifiers. To
tackle this issue and acquire faster response, Principal Component Analysis
(PCA) algorithm is used, before entering the band into the classifier. Then the
features extracted from the images are compared by three different classifiers
including Support Vector Machine (SVM), Random Forest (RF), and K-Nearest
Neighbor (KNN) classification. The results obtained from this method indicate
that the Random Forest classifier has better performance (accuracy 97%,
precision 96%, and recall 96%) than other classifiers.
