Traditionally, railway inspection and monitoring are considered a crucial aspect of the system and are done by human inspectors. Rapid progress of the machine vision-based systems enables automated and autonomous rail track detection and railway infrastructure monitoring and inspection with flexibility and ease of use. In recent years, several prototypes of vision based inspection system have been proposed, where most have various vision sensors mounted on locomotives or wagons. This paper explores the usage of the UAVs (drones) in railways and computer vision based monitoring of railway infrastructure. Employing drones for such monitoring systems enables more robust and reliable visual inspection while providing a cost effective and accurate means for monitoring of the tracks. By means of a camera placed on a drone the images of the rail tracks and the railway infrastructure are taken. On these images, the edge and feature extraction methods are applied to determine the rails. The preliminary obtained results are promising.

The temperature of rubber or rubber-metal springs increases under cyclic loading, due to hysteresis losses and low rubber thermal conductivity. Hysteresis losses correspond to energy dissipation from the rubber, which is primarily converted into heat. This well-known phenomenon, called heat build-up, is the primary reason for rubber aging. Increase in temperature within the rubber compound leads to degradation of its physical and chemical properties, increase in stiffness and loss of damping capability. This paper presents a novel procedure of heat generation prediction in rubber or rubber-metal springs. The procedure encompasses the prediction of hysteresis loss, i. e. dissipated energy within the rubber, by finite element analysis and application of a modern visco-plastic rubber constitutive model. The obtained dissipated energy was used as an input for transient thermal analysis. Verification of the proposed procedure was performed by comparison of simulation results with experimentally obtained data during the dynamic loading of the rubber specimen. The proposed procedure is highly computationally efficient and it enables time integration, which can be problematic in coupled mechanical thermal analysis.

This paper presents an on-board multi-sensor system which is able to detect obstacles and estimate their distances in railway scenes in different illumination conditions. The system was developed within the H2020 Shift2Rail project SMART (Smart Automation of Rail Transport) and aims at increasing the safety of rail transport by detecting obstacles on the rail tracks ahead of a moving train in order to reduce the number of collisions. The system hardware consists of cameras of different types integrated into a specially designed housing, mounted on the front of the train. Multiple vision sensors complement each other in order to handle different illumination and environmental conditions. The system software uses a novel machine learning-based method that is suited to a particular challenge of railway operations, the need for long-range obstacle detection and distance estimation. The development of this method used a long-range railway dataset, which was specifically generated for this project. Evaluation results of reliable obstacle detection in various environmental conditions using the SMART RGB camera in day light illumination conditions and using the SMART Night Vision sensor in poor (night) illumination conditions are presented. The results demonstrate both the potential of the on-board SMART obstacle detection system in the operational railway environment and the benefit of the use of different cameras to be more independent of light and environmental conditions.

This paper presents a numerical analysis aimed at estimating the impact of crosswinds on the aerodynamics of large vehicles using ANSYS Fluid Flow and Static Structural, offering insights into the qualitative behaviour of trucks under typical traffic conditions. The cases analysed focus on airflow distribution when a truck, traveling at approximately 100 km/h, passes under a portal while exposed to varying crosswind intensities: weak, moderate, and strong lateral winds. A detailed examination of the truck and portal reveals significant interactions between the induced airflow patterns, affecting truck handling and stability. This is evidenced by shifts in velocity and pressure distributions, changes in drag coefficients, and variations in turbulent kinetic energy, all of which are analysed and discussed in this study.

One of the most challenging technical implementations of today is self-driving vehicles. An important segment of self-driving is the ability of the computer to “see/detect” objects of interest at a distance which enables safe vehicle operation. An algorithm for the detection of railway infrastructure objects, namely, track and signals, is proposed in this paper to enable detection of signals which are relevant for the track the train is moving along. The algorithm integrates traditional computer vision (CV) algorithms, including Canny edge detection, Hough transform, and You Only Look Once (YOLO) algorithm, based on convolutional neural networks (CNNs). Each of the concepts (CV and CNNs) deals with a different object of detection which together form a unique system that aims to detect both the rails and the relevant signals. This approach ensures that the artificial intelligence (AI) system is “aware” of which route the signal belongs to. The reliability of the proposed algorithm in detection of a relevant signal, verified by the performed tests, is up to 99.7%. The metric method used for validation was intersection over union (IoU). The obtained value of IoU applied on the entire validation dataset exceeds 0.7. Calculated values of average precision and recall were 0.89 and 0.76, respectively. The algorithm created in this way solves the problem of detection of relevant signals along the train route, especially in multitrack scenarios such as stations and yards.