Road potholes detection from MLS point clouds

Abstract

The extraction of pavement damage information is one of the major difficulties in the application research of mobile laser scanning point cloud data. To address the problem of inaccurate detection results by using only relative distance to detect potholes, this paper proposes a novel pothole detection method that combines directed distance and skewed distribution. Firstly, the rapid positioning of the pothole is realized by the directed distance, which is calculated from the points and the local fitted plane. And monomerization and denoising of potential potholes are achieved by density clustering. Then, the new accurate plane is fitted by the surrounding pavement points of the potential pothole to obtain accurate directed distances. The negative skewed distribution of the directed distance histogram and the skewness coefficient are used for the accurate determination of the pothole. Finally, the three-dimensional geometric features of the pothole are extracted. Experiments were carried out on a road with poor road conditions. The experimental results validated the effectiveness and practicality of the proposed method. It can achieve automatic detection of potholes with different shapes and deformation degrees, and has effectively improved the efficiency of automatic road inspection.

Different KNN parameters on road surface extraction from UAV-based point cloud under several machine learning algorithms

“…There are two measures usually obtained when using four elements [25]. They are accuracy and quality (see equations (12) and (13). These measures range between zero (0%) and one (100%), with higher values indicating better classification results,…”

“…Various technologies and methods exist for generating 3D data. One widely used technology is the mobile laser scanning (MLS) system [12,13]. Additionally, unmanned aerial vehicles (UAVs) have recently become a popular technology for this purpose [14][15][16].…”