DCGAN ve Siyam Sinir Ağını Kullanarak Demiryolu Bağlantı Elemanlarındaki Kusurların Tespiti

Bağlantı elemanlarındaki kusurların tespiti, demiryolu denetiminin önemli bir parçasıdır. Bu nedenle son yıllarda, bağlantı elemanlarının hızlı ve güvenilir bir şekilde denetlenebilmesi için otomatik denetim sistemlerine ihtiyaç duyulmaktadır. Otomatik denetim sistemlerinde derin öğrenme gibi yöntemler kullanılmaktadır. Ancak bu tür yöntemler, eğitim için çok fazla veri setine ihtiyaç duyarlar. Geleneksel bir evrişimli sinir ağı küçük bir veri seti ile özellikleri öğrenemez. Eğitim işlemi için sağlam bağlantı elemanlarından oluşan veri setini oluşturmak kolay olmasına rağmen kusurlu bağlantı elemanlarından oluşan veri setini oluşturmak oldukça zordur. Bu tür veri setini oluşturmak için yüzlerce kilometre demiryolundan görüntü toplanması gerekebilir. Bu nedenle bu çalışmada, DCGAN kullanılarak yapay deforme bağlantı elemanı görüntüleri oluşturulup veri seti çoğaltılmıştır. Ardından, siyam sinir ağı ile bağlantı elemanlarının kusur durumu incelenmiştir. Çalışmada, sağlam ve deforme olmak üzere iki bağlantı elemanı sınıfı bulunmaktadır. Her sınıf için farklı sınıfların görüntüleri arasındaki benzerlik puanları hesaplanmıştır. Temel fikir, bağlantı elemanlarını benzerlik puanlarını kullanarak ve karşılaştırma yaparak tanımlamaktır. Deneysel sonuçlarda, önerilen yöntem için %98,23 doğruluk oranı elde edilerek, geleneksel yöntemlere göre avantajı gösterilmiştir.

Anahtar Kelimeler:

Siyam Sinir Ağı, Bağlantı Elemanı, Kusur Tespiti

Detection of Defects in Railway Fasteners Using DCGAN and Siamese Neural Network

Detection of defects in fasteners is an important part of railway inspection. Therefore, in recent years, automatic inspection systems are needed for fast and reliable inspection of fasteners. Methods such as deep learning are used in automatic control systems. However, such methods require a lot of datasets for training. A traditional convolutional neural network cannot learn features with a small dataset. Although it is easy to generate a dataset of solid fasteners for training, it is quite difficult to generate a dataset of defective fasteners. It may be necessary to collect images from hundreds of kilometers of railroad tracks to create this type of dataset. For this reason, in this study, artificial deformed fastener images were created using DCGAN and the dataset was reproduced. Then, the defect status of the connectors with the siamese neural network was examined. In the study, there are two fastener classes as healty and deformed. Similarity scores between images of different classes were calculated for each class. The basic idea is to identify fasteners using similarity scores and comparing. In the experimental results, 98,23% accuracy rate was obtained for the proposed method and its advantage over traditional methods was demonstrated.

Keywords:

Siamese Neural Network, Fastener, Defect Detection,

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