Open Access

ARTICLE

Methodology for Road Defect Detection and Administration Based on Mobile Mapping Data

by Marina Davidović^1,*, Tatjana Kuzmić¹, Dejan Vasić¹, Valentin Wich², Ansgar Brunn², Vladimir Bulatović¹

1 Department for Civil Engineering and Geodesy, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, 21000, Serbia
2 University of Applied Sciences Würzburg-Schweinfurt, Würzburg-Schweinfurt, 97070, Germany

* Corresponding Author: Marina Davidović. Email:

(This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)

Computer Modeling in Engineering & Sciences 2021, 129(1), 207-226. https://doi.org/10.32604/cmes.2021.016071

Received 03 February 2021; Accepted 03 June 2021; Issue published 24 August 2021

Abstract

A detailed inspection of roads requires highly detailed spatial data with sufficient precision to deliver an accurate geometry and to describe road defects visually. This paper presents a novel method for the detection of road defects. The input data for road defect detection included point clouds and orthomosaics gathered by mobile mapping technology. The defects were categorized in three major groups with the following geometric primitives: points, lines and polygons. The method suggests the detection of point objects from matched point clouds, panoramic images and ortho photos. Defects were mapped as point, line or polygon geometries, directly derived from orthomosaics and panoramic images. Besides the geometric position of road defects, all objects were assigned to a variety of attributes: defect type, surface material, center-of-gravity, area, length, corresponding image of the defect and degree of damage. A spatial dataset comprising defect values with a matching data type was created to perform the attribute analysis quickly and correctly. The final product is a spatial vector data set, consisting of points, lines and polygons, which contains attributes with further information and geometry. This paper demonstrates that mobile mapping suits a large-scale feature extraction of road infrastructure defects. By its simplicity and flexibility, the presented methodology allows it to be easily adapted to extract further feature types with their attributes. This makes the proposed approach a vital tool for data extraction settings with multiple mobile mapping data analysts, e.g., offline crowdsourcing.

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Citations