Fast Train-borne Ballast Geometry Degradation Inspection via Point Cloud Diff-graph Analysis

To address the demand for efficient detection of ballast geometry degradation in China's railways, this paper proposes a fast train-borne inspection method based on point cloud diff-graph analysis method (DGAM). The method converts train-borne LiDAR point clouds into diff-graphs, integrates a standard ballast template, and employs a diff-graph projection module (DGPM) to extract geometric deviations. It further utilizes Michelson-like contrast (MLC) enhancement to suppress false defects caused by template mismatch. Subsequently, a defect detection module (DDM) is applied to achieve precise defect localization. Experimental results demonstrate that DGAM achieves high precision, with IoU exceeding 80.70%, and high speed, supporting inspection at 160 km/h in the simulation tests, outperforming traditional contrast enhancement methods. It has been integrated into a high-speed inspection vehicle, providing an effective technical solution for railway ballast maintenance.