Automatic Diagnosis of Railway Track Elements Using Deep Learning Neural Networks

The article discusses a method for automatic diagnostics of a railway track. It consists in automatic evaluation of the technical condition of selected track elements, such as rails, wooden and concrete sleepers, fasteners and turnouts. It was carried out on the basis of analysis of video images of railroad track elements recorded by two line cameras placed on the diagnostic carriage. The selected FCN-8 deep learning neural network was used to assess the technical condition of the surveyed elements, and the effectiveness of the applied algorithm was determined on the basis of such measures as IoU, Precision, Recall. Conclusions on the application of the FCN-8 network in the automatic classification of features of selected railroad track elements are presented. The results obtained were compared with other methods used in vision diagnostics.