Reznitskiy M.A., Arshinsiy L.V. Programmnaya realizatsiya avtomatizirovannoi systemi obnaryjeniya defectov verkhnego cttroeniya puti na osnove tekhnologii svertochnykh neyronnykh setey [Software implementation of an automated system for detecting defects of the upper structure of the railway path based on the technology of convolutional neural networks]. Molodaya nauka Sibiri: ehlektronnyj nauchnyj zhurnal [Young science of Siberia: electronic scientific journal], 2018, no. 1. [Accessed 24/10/18]
In the article, based on the technology of convolutional neural networks, approaches to solving the problem of automated processing of images of the upper structure of the railway track in order to identify areas suspicious of defects are considered. Such defects are considered as defects, rail joints and defects, rail fasteners, sleepers defects, surface defects in rails, "other", which include all the defects, not included in the previous classification. At the same time, due to the high cost of the second kind of error, all images with the help of the network are divided into two classes: images without defects and images that are suspicious of defects. The second set of images is proposed to be sent to specialists for manual verification. The source data for the image processing process are files with snapshots of the paths. Each photo represents a shift relative to the previous one by about 30 cm along the paths. The images received by one of trains of the center of diagnostics and monitoring of devices of infra-structure – structural division of the West Siberian management of infrastructure-structural division of the West Siberian railway – branch of JSC RZHD were taken as a basis. The content and features of the method and its software implementation are discussed. The software package includes a program for automated image segmentation, a program for automated Assembly of training and test data sampling into a text file, a program for configuration and training of a convolutional neural network. The principal possibility of using convolutional neural networks for solving the given class of problems is shown.
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