This paper presents the results of testing the physical and mechanical properties and durability of geopolymer mixtures. Fly ash was used as the basic binder, while its replacement was performed with wood biomass ash.The initial and final setting tim...

For reliable autonomous train operation, detecting and classifying obstacles on or near rail tracks, and accurately estimating the distance to these obstacles, is essential. This task is more challenging in low-light conditions, common for freight trains that operate primarily at night. This paper proposes a novel method, FuzzyH, for estimating the distance between a thermal camera and detected obstacles using image-plane homography. By leveraging the homography between the image and rail track planes, and incorporating a fuzzy logic system, the method improves distance estimation accuracy and eliminates the need for complex calibration. This paper also explores the symmetry and asymmetry of fuzzy membership functions and rules. The system was validated on Serbian railways under simulated real-world conditions, demonstrating reliable performance. A key contribution of this method is the use of fuzzy membership functions tailored to specific distance ranges, based on experimental data and domain knowledge, such as regulatory braking distances. This approach improves over traditional methods by offering reliable distance estimates in low-light environments and simplifying the calibration process, ultimately enhancing system accuracy and robustness.

Human presence in rail track areas represents a critical safety risk, particularly under low-visibility conditions where RGB-based monitoring systems become unreliable. This paper presents a thermal-only railway monitoring framework for human detection, auxiliary pose-based skeletal representation, identity-preserving tracking, temporal refinement, track zone classification, and camera-to-human distance estimation. The proposed approach combines a YOLO-based human detector trained on annotated thermal railway images with a pretrained YOLO pose model used as an auxiliary module to obtain a reduced 13-keypoint skeletal representation for thermal-image interpretation. ByteTrack is used for identity association across frames, while Kalman filtering reduces frame-to-frame keypoint localization jitter and supports short-term trajectory continuity during temporary missed detections. Unlike fully learned monocular depth estimation, the proposed distance estimation method exploits rail track geometry as a physical scene reference. A YOLO-based rail track detection model extracts the rail region, after which rail line candidates are fitted, and the apparent rail track width is measured at the vertical position of the detected human. Distance is estimated using an inverse-perspective relationship based on the known railway gauge. Experiments on thermal video sequences acquired along the Niš–Prokuplje railway line demonstrate reliable human detection, stable tracking, effective track zone classification, and distance estimation consistent with real distance trends.