PROJECT TITLE :

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

ABSTRACT:

This paper proposes a completely unique framework for automated detection of urban road manhole covers using mobile laser scanning (MLS) data. First, to narrow looking out regions and cut back the computational complexity, road surface points are segmented from a raw purpose cloud via a curb-based mostly 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 options of native image patches. Next, a random forest model is trained to learn mappings from high-order patch options to the chances of the existence of urban road manhole covers focused 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 a mean completeness, correctness, quality, and $mathrmF_1$-measure of zero.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 fast and automatic detection of urban road manhole covers using MLS information.