Automated Detection of Urban Road Manhole Covers Using Mobile Laser Scanning Data

This paper proposes a novel framework for automated detection of urban road manhole covers using mobile laser scanning (MLS) data. First, to narrow searching regions and reduce the computational complexity, road surface points are segmented from a raw point cloud via a curb-based road surface segmentation approach and rasterized into a georeferenced intensity image through inverse distance weighted interpolation. Then, a supervised deep learning model is developed to construct a multilayer feature generation model for depicting high-order features of local image patches. Next, a random forest model is trained to learn mappings from high-order patch features to the probabilities of the existence of urban road manhole covers centered at specific locations. Finally, urban road manhole covers are detected from georeferenced intensity images based on the multilayer feature generation model and random forest model. Quantitative evaluations show that the proposed algorithm achieves an average completeness, correctness, quality, and F₁-measure of 0.955, 0.959, 0.917, and 0.957, respectively, in detecting urban road manhole covers from georeferenced intensity images. Comparative studies demonstrate the advantageous performance of the proposed algorithm over other existing methods for rapid and automated detection of urban road manhole covers using MLS data.