Automatic Detection Method of Pavement Deformation Distress Based on Point Cloud Data

Abstract:

Shoving and subsidence are common pavement distresses. However， the traditional judgment method based on two-dimensional image cannot obtain depth information. The linear laser scanning method is precise， yet the single scan range is limited. Thus， it is difficult to obtain the global situation in a short time when identifying deformation distresses. A method is proposed for detecting deformation distresses and extracting their 3D features by using a vehicle-mounted mobile lidar system. Point cloud data are segmented and abnormal deformation points are extracted. 3D features of deformation distress are obtained. The reliability and effectiveness of the method are verified by the measured data. The results show that the method can automatically detect the deformation such as shoving， subsidence， and potholes. It can effectively improve the detection efficiency. Compared with the total station measurement results， the 3D feature extraction results are complete with an accuracy of 84.662%.