Mask R-CNN Derin Sinir Ağı ile Demiryolu Hattı Bileşenlerinde Çoklu Hata Tespiti

Year 2022, , 1103 - 1112, 30.12.2022

Merve Yılmazer Mehmet Karaköse İlhan Aydın Erhan Akın

https://doi.org/10.21605/cukurovaumfd.1230955

Cited By: 1

Abstract

Demiryolu birçok yolcunun aynı anda seyahat edebilmesine olanak tanıyan aynı zamanda yük taşımacılığında da sıklıkla kullanılan bir ulaşım çeşididir. Ulaşımda kazalar ve aksamalar meydana gelmemesi için hattın periyodik olarak kontrolünün sağlanması ve hatalı bileşenlerin belirlenerek onarılması gerekmektedir. Raylı ulaşım sistemlerinin güvenliğinin sağlanması için yapılan manuel denetimlere alternatif olarak, son zamanlarda görüntü işleme algoritmaları ve derin öğrenme algoritmaları kullanılarak temassız, hızlı ve güvenilir sonuçlar veren hata tespit yöntemleri geliştirilmiştir. Bu çalışmada sağlıklı olan traversler ve travers üzerinde meydana gelen çeşitli hataların tespit edilmesine yönelik Mask R-CNN derin sinir ağı mimarisi kullanılarak yeni bir yöntem önerildi. Üç farklı hata türü ve sağlıklı travers olmak üzere toplamda dört farklı sınıf etiketi ile etiketlenen gerçek demiryolu görüntüleri kullanılarak model eğitimi ve eğitilen modelin test edilmesi sağlandı. Değerlendirme metrikleri hesaplanarak modelin başarı performansı ölçüldü. Sağlıklı ve hatalı olan traversleri belirlemede modelin doğruluğu %95 olarak belirlendi.

Keywords

Demiryolu traversi, Mask r-cnn, Arıza tespiti, Otonom drone, Railway sleeper, Fault detection, Autonomous drone

Multiple Fault Detection in Railway Components with Mask R-CNN Deep Neural Network

Abstract

Railway is a type of transportation that allows many passengers to travel at the same time and is often used in freight transportation. In order to prevent accidents and disruptions in transportation, the line is checked periodically, faulty components are determined and repaired or replaced with new ones. As an alternative to manual inspections to ensure the safety of rail transportation systems, defect detection methods that provide contactless, fast and reliable results have been developed recently by using image processing algorithms and deep learning algorithms. In this study, a new method is proposed using Mask R-CNN deep neural network architecture to detect healthy sleepers and various faults on the sleeper. Model training and testing of the trained model were provided by using real railway images labeled with four different class labels, three different error types and healthy sleeper. The success performance of the model was measured by calculating the evaluation metrics. The accuracy of the model was determined as 95% in determining the healthy and faulty sleepers

Keywords

Mask r-cnn, Railway sleeper, Fault detection, Autonomous drone

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