Application of point cloud data in geometric detection of critical nodes of turnouts
The process of detecting the operational status of a turnout is complex, and traditional methods require the use of rail inspection vehicles, rail gauges, support gauges, and height gauges for measurement. The equipment types are varied, the measurement process is lengthy, and there is a high demand for the time window. To improve detection efficiency, this paper proposes a key node measurement method for single turnout based on point clouds. The method utilizes CAD graphic elements and graph convolutional neural networks to achieve the precise, automated identification, segmentation, and extraction of the three-dimensional point cloud data of the turnout structure, with an accuracy rate of 99. 68％. At the same time, by combining the geometric prior information of the turnout structure, the key geometric position parameters such as the track gauge, curve radius, support spacing, and rail elevation drop are accurately extracted in a rapid and precise manner. Verified by examples, the measurement results of the key geometric position detection method for turnouts based on point cloud proposed in this paper have a sub-millimeter error with the real value, which meets the requirements of actual engineering inspection, eliminates a variety of inspection equipment and saves a lot of time for skylight, which has a high degree of practicability, and it is the development trend of future turnout measurements.
3D point cloud dataturnoutoperating statusconvolutional neural networklaser scanning
万方数据
维普
