Demiryolu Bağlantı Elemanlarında Bulunan Kusurların YOLOv4 ve Bulanık Mantık Kullanarak Tespiti

Year 2021, Issue: 14, 249 - 262, 31.07.2021

Emre Güçlü , İlhan Aydın , Kadir Şahbaz Erhan Akın , Mehmet Karaköse

https://doi.org/10.47072/demiryolu.939830

Cited By: 5

Abstract

Demiryollarında bulunan bağlantı elemanları, raylı sistemlerin en önemli bileşenidir. Bağlantı elemanları, diğer bileşenleri birbirine bağlar ve trenin raylar üzerinde güvenli bir şekilde hareket etmesini sağlar. Bu nedenle, hasarlı bağlantı elemanlarının tespiti, demiryolu taşımacılığının güvenliğini sağlamak için önemlidir. Bağlantı elemanlarının kontrolü genellikle eğitimli çalışanlar tarafından görsel olarak yapılır. Güvenlik standartlarını sağlayabilmek için binlerce kilometrelik hat, insanlar tarafından denetlenmelidir. Ancak bu yöntem, hız açısından oldukça sınırlıdır ve ihmallere neden olabilir. Bu nedenle, otomatik denetim sistemlerinin geliştirilmesine ihtiyaç vardır. Bu çalışmada, kırık bağlantı elemanlarını tespit etmek için, YOLOv4 ve bulanık mantık yapısına dayanan yeni bir yöntem önerilmiştir. Bağlantı elemanı görüntüsü 6 ayrı parçaya bölünerek etiketlenmiştir. Bağlantı elemanı görüntülerine YOLOv4 algoritmasının uygulanması ile 6 parçanın güven değerleri oluşturulmuştur. Oluşan 6 farklı güven değeri bulanık mantık yapısı için giriş değeri olarak verilmiştir ve bağlantı elemanının sağlık durumu hakkında yüzde cinsinden sonuç değeri üretilmiştir. Deney sonuçları, doğru tespit oranının %99 üzerinde olduğunu göstermiştir.

Keywords

Demiryolları, Bağlantı Elemanı, YOLOv4, Bulanık Mantık

Supporting Institution

TÜRKİYE BİLİMSEL VE TEKNOLOJİK ARAŞTIRMA KURUMU

Project Number

120E097

Detection of Defects in Railway Fasteners Using YOLOv4 and Fuzzy Logic

Abstract

Fasteners in railways are the most important component of rail systems. Fasteners clamp other components together and allow the train to move safely on rails. Therefore, the detection of damaged fasteners is important to ensure the safety of rail transport. Detection of fasteners is usually done visually by trained employees. Thousands of kilometers of line must be inspected by people to ensure safety standards. However, this method is very limited in terms of speed and can cause negligence. Therefore, there is a need for the development of automatic control systems. In this study, a new method based on YOLOv4 and fuzzy logic is proposed to detect broken fasteners. The fastener image is divided into 6 separate parts and labeled. Confidence values of 6 parts were created by applying the YOLOv4 algorithm to the fastener images. The resulting 6 different confidence values are given as the input value for the fuzzy logic structure and a result value in percent about the health status of the fastener is produced. Experiment results showed that the correct detection rate was over 99%.

Keywords

Railways, Fastener, YOLOv4, Fuzzy logic

Project Number

120E097

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