A New Computer Vision Based Rail Detection Method Using Entropy and Support Vector Machines





classification, entropy, support vector machine, image processing, railways


     Condition monitoring in railways is an important and critical process in terms of travel safety. However, this process is
generally done based on observation or with various equipment. Therefore, it is costly and has a high probability of error. In this
study, a computer vision-based method for rail detection for condition monitoring in railways is proposed. In addition to the
features obtained from the images, a new feature is calculated using entropy. Rail detection is provided by classifying these
features with Support Vector Machine (SVM). It has been seen that the proposed method works successfully and provides
improvement in the monitoring process.



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How to Cite

Murat, K., Karaköse, M., & Akın, E. (2023). A New Computer Vision Based Rail Detection Method Using Entropy and Support Vector Machines. International Journal of Computer and Information Technology(2279-0764), 12(2). https://doi.org/10.24203/ijcit.v12i2.331