YOLOv5 ile Topluluk Öğrenmesine Dayalı Olarak Ray Yüzeyindeki Kusurların Tespiti

Year 2023, Issue: 17, 115 - 132, 31.01.2023

Mehmet Sevi , İlhan Aydın , Erhan Akın

https://doi.org/10.47072/demiryolu.1205483

Cited By: 3

Abstract

Demiryolu ulaşımı son yıllarda demiryolu hat uzunluğunun artmasıyla beraber kapasitesini arttırmıştır. Hızlı trenlerin gelişmesi de bu duruma katkı sağlamıştır. Yolcu ve yük kapasitesinin artması güvenlik tedbirlerinin önemini daha da arttırmıştır. Demiryolu hatlarının güvenliğini sağlamak için hatların belirli aralıklarla denetlenmesi gerekmektedir. Demiryolu hattı bakımında ray üzerinde bulunan kusurların tespiti son derece önemlidir. Bu çalışmada demiryolu bakımının önemli bir parçası olan ray bileşeni üzerindeki kusurların tespitine odaklanılmıştır. Çalışmada ray üzerinde bulunan kusurları bir nesne tespiti yöntemi olan YOLO ile tespit etme yoluna gidilmiştir. Farklı YOLO modelleri için topluluk öğrenmesine dayalı bir yöntem önerilmiştir. Deney sonuçları, 8 farklı kusur içeren veri seti üzerinde bütün sınıfları içeren tespit oranının %80’in üzerinde olduğunu göstermiştir.

Keywords

Demiryolu, Ray bağlantı elemanları, Kusur tespiti, Bilgisayarlı görme, YOLO, Derin öğrenme algoritmaları., Railway, Rail fasteners, Defect detection, Computer vision, Deep learning algorithms

Supporting Institution

TÜBİTAK

Project Number

120E097

Thanks

Bu çalışma, 120E097 numaralı TÜBİTAK projesi tarafından desteklenmiştir.

Detection of Rail Surface Defects Based on Ensemble Learning of YOLOv5

Abstract

Railway transportation has increased its capacity with the increase in railway line length in recent years. The development of high-speed trains also contributed to this situation. The increase in passenger and cargo capacity has further increased the importance of security measures. In order to ensure the safety of the railway line, it is necessary to inspect the line at certain intervals. Detection of defects on the rail is extremely important in the maintenance of the railway line. This study focuses on the detection of defects on the rail component, which is an important part of railway maintenance. In the study, it was tried to detect the defects on the rail with YOLO, which is an object detection method. In the study, it has been shown that model ensembling gives better results than YOLO models that validate alone. A method based on ensemble learning is proposed for different YOLO models. Experiment results showed that the detection rate including all classes on the data set containing 8 different defects was over 80%.

Keywords

Demiryolu, Ray bağlantı elemanları, Kusur tespiti, Bilgisayarlı görme, YOLO, Derin öğrenme algoritmaları., Railway, Rail fasteners, Defect detection, Computer vision, Deep learning algorithms

Project Number

120E097

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