Classification of 3D Point Cloud Data from Mobile Mapping System for Detecting Road Surfaces and Potholes using Convolution Neural Networks

Abstract

Normally, road damages can be automatically detected using image and video data from ground survey vehicle system combined with the detection algorithms. However, there are limitations of scales and map coordinates when using the image and video data to detect potholes. It has been challenging to detect and determine the sizes and locations of potholes. This research utilized a mobile mapping system, MMS, to collect data of roads and environment and classify potholes, roads and other objects. A convolution neural network (CNN) was used to directly identify 3D point clouds using the XYZ method in comparison with the proposed XYZ-RGB method. The XYZ classification demonstrated an overall accuracy of 96.77%, with the intersection over union (IoU) of potholes, roads, and other objects of 59.50%, 94.22%, and 94.06%, respectively. The proposed XYZ-RGB classification indicated an overall accuracy of 97.50%, with the IoU of potholes, roads, and other objects of 66.66%, 95.43%, and 95.42%, respectively. Both datasets were statistically compared at the 95% confidence level, and the results revealed that both classifications produced significantly different results.