Point Cloud-Based Pavement Crack Extraction Using MSAC and KNN Algorithm

Crack evaluation is critical to the pavement condition acquisition and preservation. The current pavement crack detection method based on two-dimensional images is relatively mature, but the extraction accuracy is quite dependent on the quality of the images themselves, which is greatly affected by various conditions. To tackle these problems, this paper proposes a pavement crack extraction method based on three-dimensional laser point cloud, which jointly uses the M-estimator sample consensus (MSAC) algorithm and K-nearest neighbor (KNN) algorithm. According to the feature that most of the crack points are below the road surface, the MSAC algorithm is used to fit the pre-processed point cloud to the plane and separate the crack point cloud from the pavement texture points. Experimental results exhibit that the fitting effect is better when the maximum point-to-plane distance is 6 mm. The distance density is characterized by the nearest neighbor distance of each point calculated by KNN algorithm. Results show that it is better to use 0.95 times the average distance as the threshold to filter the crack area. The YOLOv5 deep learning model is conducted to identify the crack images, and the results indicate that the proposed method has a good performance in reproducing the morphology of cracks.