IDENTIFICATION OF RELEVANT POINT CLOUD GEOMETRIC FEATURES FOR THE DETECTION OF PAVEMENT CRACKS USING MLS DATA

Abstract. The maintenance of road infrastructures is one of the main challenges that transportation authorities must face to guarantee the safe mobility of people and goods. Novel remote monitoring technologies offer advanced solutions for this issue, allowing the inspection of large sections of the network in a time-effective way. In this paper, we introduce a methodology for the detection of cracks on road pavements using point clouds acquired with a mobile laser scanner. First, the points of the cloud are labelled as pavement or cracks based on field annotations, and local geometric features of the points are calculated using principal component analysis. Two different machine learning classifiers, Support Vector Machine (SVM) and Random Forest, are then trained to identify crack points using the point feature data. The crack points predicted by the classifiers are clustered as individual instances and compared to the corresponding ones from a test dataset. Although pointwise performance of the method is modest, it can correctly identify and measure areas of the pavement affected by cracking.