The article presents a vision system for detecting elements of railway track. Four types of fasteners, wooden and concrete sleepers, rails, and turnouts can be recognized by our system. In addition, it is possible to determine the degree of sleeper ballast coverage. Our system is also able to work when the track is moderately covered by snow. We used a Fully Convolutional Neural Network with 8 times upsampling (FCN-8) to detect railway track elements. In order to speed up training and improve performance of the model, a pre-trained deep convolutional neural network developed by Oxford’s Visual Geometry Group (VGG16) was used as a framework for our system. We also verified the invariance of our system to changes in brightness. To do this, we artificially varied the brightness of images. We performed two types of tests. In the first test, we changed the brightness by a constant value for the whole analyzed image. In the second test, we changed the brightness according to a predefined distribution corresponding to Gaussian function.

The amount of freight transported by rail and the number of passengers are increasing year by year. Any disruption to the passenger or freight transport stream can generate both financial and human losses. Such a disruption can be caused by the rail infrastructure being in poor condition. For this reason, the state of the infrastructure should be monitored periodically. One of the important elements of railroad infrastructure is the ballast. Its condition has a significant impact on the safety of rail traffic. The unevenness of the ballast surface is one of the indicators of its condition. For this reason, a regulation was introduced by Polish railway lines specifying the maximum threshold of ballast unevenness. This article presents an algorithm that allows for the detection of irregularities in the ballast. These irregularities are determined relative to the surface of the sleepers. The images used by the algorithm were captured by a laser triangulation system placed on a rail inspection vehicle managed by the Polish railway lines. The proposed solution has the following elements of novelty: (a) it presents a simple criterion for evaluating the condition of the ballast based on the measurement of its unevenness in relation to the level of the sleeper; (b) it treats ballast irregularity detection as an instance segmentation process and it compares two segmentation algorithms, Mask R-CNN and YOLACT, in terms of their application to ballast irregularity detection; and (c) it uses segmentation-related metrics—mAP (Mean Average Precision), IoU (Intersection over Union) and Pixel Accuracy—to evaluate the quality of the detection of ballast irregularity.

The digital transformation of industry, referred to as Industry 4.0, is increasing the importance of virtualizing industrial processes. As technology advances, industrial production is becoming more complex and requires more efficient management tools. Industrial process virtualization is one such tool that allows industrial processes to be accurately simulated in order to better understand and optimise them. This undoubtedly has an impact on the approach to the design and subsequent implementation of production automation investments. This paper presents an analysis of the possibility of using, in the process of simulating industrial automation systems, platforms such as Factory I/O and PLCSIM software, which is a component of TIA Portal. For this purpose, a control and visualisation system for a product sorting line was implemented in the aforementioned environments. The research carried out confirmed the very high suitability of the Factory I/O platform for the creation of 3D simulations and virtualisation of industrial processes, as well as the possibility of integrating this platform with the TIA Portal software for PLC programming.

Effective engineering management of railway disruptions requires a transparent framework that links operating-cost optimisation, detour feasibility and service continuity constraints under uncertain repair durations. This paper presents IAAK (Intelligent Cost Analysis Algorithm), a baseline-gated decision-support framework demonstrated on a selected regional railway line. The deterministic layer computes passenger and freight cost thresholds, while the AI advisory layer is validated on a temporally separated dispatcher decision set and does not replace dispatcher judgement. For railway line No. 30, the framework identifies a direct freight-detour activation threshold of approximately 9.3 h and an upper bound return-plus-detour threshold of approximately 50.6 h. The multi-threshold route inventory is complemented by a 130-case dispatcher decision set for 26 Lubelskie and adjoining lines, with 104 training cases up to 31 December 2025 and 26 test cases from 1 January 2026 to 16 June 2026. On this temporal hold-out, GB and RF reproduce the action classes, while the PNN/RBF comparator reaches macro F1 = 0.927 and balanced accuracy = 0.952. The results improve coverage, temporal validation and operational grounding for dispatcher review. Methodological novelty lies in coupling a hard admissibility gate, deterministic break-even costs, post gate multicriteria ordering and a non-authoritative AI audit in one dispatcher review record rather than in proposing a new cost formula or classifier. On the 26-case temporal hold-out, GB and RF add no action-level disagreement, while PNN/RBF flags 2 of 26 cases; the AI layer is therefore interpreted as an audit channel, not as an accuracy improvement over the deterministic gate. CDRA uses an externally specified scenario probability in the implemented study, whereas probability estimation and online updating remain future extensions. All 26 test lines also occur in the training partition, and the 50.6 h upper bound threshold exceeds the longest recorded closure; these findings are consequently interpreted as within-line scenario evidence rather than cross-network-predictive or empirical-threshold validation.