The ability to detect, localize and classify objects that are anomalies is a challenging task in the computer vision community. In this paper, we tackle these tasks developing a framework to automatically inspect the railway during the night. Specifically, it is able to predict the presence, the image coordinates and the class of obstacles. To deal with the lowlight environment, the framework is based on thermal images and consists of three different modules that address the problem of detecting anomalies, predicting their image coordinates and classifying them. Moreover, due to the absolute lack of publiclyreleased datasets collected in the railway context for anomaly detection, we introduce a new multi-modal dataset, acquired from a rail drone, used to evaluate the proposed framework. Experimental results confirm the accuracy of the framework and its suitability, in terms of computational load, performance, and inference time, to be implemented on a self-powered inspection system.
The new high speed railway lines, under construction in Italy, are electrified in 2x25 kHz -50 Hz and adopt a signaling system with audio frequency track circuits (AF-TC), ranging from 2.1 kHz to 16.5 kHz. On this type of TC preliminary studies of the compatibility are required in order to test their correct operation. To this aim a measurement campaign on the AF-TC of the new High Speed Railway System Rome-Naples has been made. From the measurement results and their post-elaboration, based on accuracy and uncertainty analysis, the AF-TC voltage profile has been drawn with some interesting consideration about the operation of the AF-TC, that are reported in the present paper.
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