Ray Bileşenlerinde Meydana Gelen Arızaların Görüntü İşleme Teknikleri ile Tespit Edilmesi

Bu çalışmada görüntü işleme teknikleri kullanılarak demiryollarında kullanılan ray, baglantı noktaları, travers gibi bileşenlerde oluşan, kusurların ve hataların tespiti gerçekleştirilmiştir. Demiryolu bileşenlerinde oluşan hataların erken tespiti yapılarak, bu hataların hızlı ve etkin bir şekilde ortadan kaldırılması ve bu hatalardan dolayı oluşabilecek kazaların ve kayıpların önüne geçilmesi amaçlanmıştır. Bu kapsamda demiryolu bileşenlerinden olan ray görüntülerinden bağlantı noktasında vidası olmayan, sıkma aparatı dönmüş veya çıkmış olan, ray çatlakları ve travers çatlakları bulunan görüntülerden oluşan 4 farklı bileşenden 7 farklı problem tespit edilerek çalışma gerçekleştirilmiştir. Elde edilen görüntülerden öncelikle SIFT, SURF, GLCM, LBP ve HOG olmak üzere 5 farklı öznitelik çıkarım yöntemi kullanılarak öznitelikler elde edilmiştir. Daha sonra elde edilen öznitelik vektörleri kullanılarak Decision Tree (DT), Gradient Boosting Classifier (GBC), Linear Discriminant Analysis (LDA), SVM, SVC, Logistic Regression (LR), Naive Bayes (NB), Nearest Neighbors(Knn), Neural Net (NN) ve Random Forest(RF) gibi 10 farklı makine öğrenmesi yöntemleri ile sınıflandırma işlemleri gerçekleştirilmiştir. HOG kullanılarak çıkarılan özniteliklerden SVM sınıflandırma yöntemi ile %98 oranında başarı gözlenmiştir.

Detection of Faults in Rail Components with Image Processing Techniques

In this study, image processing method were utilized for the early detection of defects and faults in the rails used on the railways and the components around these rails. Through early detection of the failures occurring in the railway components, this study aims to remove these failures rapidly and in an effective way; and prevent probable accidents and losses that may occur. In this context, seven particular problems have been identified from four diverse components comprised of the images that no screw situated at the junction point, clamping device rotated or extracted, rail and traverse cracks exist. From the images obtained, the features were obtained by using 5 different feature extraction methods: SIFT, SURF, GLCM, LBP and HOG. Then, using the feature vectors, Classification procedures were carried out with 10 different machine learning methods such as Decision Tree (DT), Gradient Boosting Classifier (GBC), Linear Discriminant Analysis (LDA), SVM, SVC, Logistic Regression (LR), Naive Bayes (NB), Nearest Neighbors (Knn), Neural Net (NN) and Random Forest (RF). 98% success was observed with the SVM classification method, which is one of the features extracted using HOG.

Keywords:

Railways, cracked-broken rails, surf, sift, glcm hog, lbp,

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