High-precision detection of wheel dimensions and tread wear using stereo vision and advanced 3D measurement algorithms for railway safety

Accurate in-motion inspection of railway wheels requires reliable estimation of wheel diameter, flange geometry, and localized tread spalling under partial views, noise, and surface wear. Many existing systems either infer wheel condition indirectly, measure only a subset of parameters, or suffer from ambiguous alignment on nearly axis-symmetric wheel surfaces and biased diameter fitting when defect points are included. These issues limit practical wayside deployment where observations are sparse and partially occluded. This paper presents a stereo-vision sensing system and an integrated 3D measurement pipeline for high-precision wheel condition assessment. The proposed approach is novel, to our knowledge, in combining keypoint-constrained congruent-set registration to resolve near-symmetry ambiguity with rim-corner plane-circle fitting to suppress wear/defect-induced bias in rolling-radius estimation. Coarse alignment is obtained via constrained congruent-set sampling and refined by least-squares pose optimization to fuse sparse observations into a consistent wheel model. Wheel diameter is estimated from rim-corner points, while flange height and thickness are extracted using projection-based interval sampling and NURBS profile fitting. Spalling regions are segmented by boundary extraction and clustering, and spalling length and depth are quantified through cylindrical projection. Experiments validated against gauge-based ground truth showed mean absolute errors of 0.08 mm (rolling radius), 0.05 mm (flange height), 0.06 mm (flange thickness), 0.02 mm (spalling length), and 0.09 mm (spalling depth), and reduced rolling-radius error by 38.5% compared with a conventional baseline. The results demonstrate a practical, submillimeter solution for in-motion wheel condition monitoring.