Broken Rail Detection With Texture Image Processing Using Two-Dimensional Gray Level Co-occurrence Matrix

• URL: http://arxiv.org/abs/2304.11592v1
• Date: Sun, 23 Apr 2023 09:44:32 GMT
• Title: Broken Rail Detection With Texture Image Processing Using Two-Dimensional Gray Level Co-occurrence Matrix
• Abstract summary: Rail damage and breakage is one of the most important causes of accidents on the railway. In this paper, we have proposed a method that the rail region is first recognized from the observation area. The types of rail defects including cracks, wear, peeling, disintegration, and breakage are detected.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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