3D Mapping of Pavement Distresses Using an Unmanned Aerial Vehicle (UAV) System

Leonardi, Giovanni; Barrile, Vincenzo; Palamara, Rocco; Suraci, Federica; Candela, Gabriele

doi:10.1007/978-3-319-92102-0_18

Cited by 19 publications

(10 citation statements)

References 5 publications

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“…A UAV is an aerial vehicle that can fly without a human pilot [36]. UAVs have become extremely popular, especially in civil applications, due to their low cost and practicality [36][37][38][39]. A growing area of interest for UAV applications is the periodic inspection and evaluation of infrastructures, which is currently an expensive and time-consuming task [40].…”

Section: New Technologies For Inspecting Pavement Deteriorationmentioning

confidence: 99%

Analysis of Optimal Flight Parameters of Unmanned Aerial Vehicles (UAVs) for Detecting Potholes in Pavements

et al. 2020

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Pavement maintenance seeks to provide optimal service conditions. Before maintenance, it is necessary to know the condition of the pavement by inspection, a crucial step in deciding on the repair to be carried out. In this sense, unmanned aerial vehicles (UAVs) seem to be an economic substitute compared to the ground laser scanner for pavement inspection tasks. This research seeks to develop a method to measure potholes using 3D models generated with photographs acquired by a UAV and process them using a software based on the Structure from Motion-MultiView Stereo (SfM–MVS) technique. The contribution of this document is the proposal of recommendations for the acquisition of photographs for the realization of the models. To develop these recommendations, an experiment was carried out to evaluate the accuracy in the reconstruction of 3D models using images obtained from the variation and combination of flight planning parameters and data capture. Then, to validate these recommendations, a bumpy section of pavement was modeled using the SfM–MVS method. The results show that for heights of 10 and 15 m the use of this methodology is applicable for the measurement of the width and depth of potholes.

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Section: New Technologies For Inspecting Pavement Deteriorationmentioning

confidence: 99%

Analysis of Optimal Flight Parameters of Unmanned Aerial Vehicles (UAVs) for Detecting Potholes in Pavements

et al. 2020

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“…Consequently, new technologies have been implemented to facilitate more efficient inspection of structural damage. The use of unmanned aircraft vehicles (UAV) equipped with high-resolution cameras has become relevant due to their ability to capture images in places that may be difficult to access, in addition to their efficiency and the low associated cost [4]. The data acquired through these systems can then be used to build virtual models through photogrammetry techniques [5].…”

Section: Introductionmentioning

confidence: 99%

Calibration of UAV Flight Parameters to Inspect the Deterioration of Heritage Façades Using Orthogonal Arrays

et al. 2022

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Heritage façades are relevant to the historical preservation and identity of a city. Their deterioration can cause the devaluation of the urban environment, together with accidents caused by possible detachment of their different elements; therefore, maintaining them is crucial. It is important to have early warning systems in place, systems which would indicate potential damage and deterioration of such façades, which can be caused by factors such as humidity, earthquakes, the passage of time, or even people. This is often done through visual inspection; however, visual inspection is often affected by external factors, such as the financing of the on-site inspection and the availability of experts. Unmanned aircraft vehicles (UAVs) are a practical and economical tool that has facilitated and improved imaging by regulating parameters such as camera angle, capture distance and image overlap, thus allowing improvements in photogrammetry techniques for virtual reconstruction processes. Although the implementation of this technology has been studied in recent years, research has focused primarily on horizontal structures, such as roads. In the case of vertical structures, flight parameters are traditionally defined by trial and error, without any methodological procedure. Therefore, this paper proposes a methodology to calibrate UAV flight parameters to build a photogrammetric 3D model of a vertical structure. This proposal is based on the development of a series of experiments configured through Taguchi orthogonal arrays, which is a method that significantly reduces the number of experiments required. This methodology is applied to a case study of a façade with architectural heritage features. The results obtained offer a flight plan and the optimal parameters to achieve efficiency in the capture of images in an inspection of heritage façades, thus achieving the optimisation of resources and time.

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“…Marra et al [11] show how the rut depth in a forest path is measured from the automatically created point cloud [11]. Several authors have used UAV to assess pavement distress through image processing, however they did not include rut depth measurements [12][13][14]. Existing grass, logging residues, and water along ruts are referred to as the main factors that compromise the precision of the photogrammetric methods.…”

Section: Introductionmentioning

confidence: 99%

Road Rutting Measurement Using Mobile LiDAR Systems Point Cloud

Gézero

Antunes

2019

IJGI

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Road rutting caused by vehicle loading in the wheel path is a major form of asphalt pavement distress. Hydroplaning and loss of skid resistance are directly related to high road rutting severity. Periodical measurements of rut depth are crucial to maintenance and rehabilitation planning. In this study, we explored the feasibility of using point clouds gathered by Mobile LiDAR systems to measure the rut depth. These point clouds that are collected along roads are usually used for other purposes, namely asset inventory or topographic survey. Taking advantage of available clouds to identify rutting severity in critical pavement areas can result in considerable economic and time saving and thus, added value, when compared with specific expensive rut measuring systems. Four different strategies of cloud points aggregation are presented to create the cross-section of points. Such strategies were established to improve the precision of individual sensor measurements. Despite the 5 mm precision of the used system, it was possible to estimate rut depth values that were slightly inferior. The rut depth values obtained from each cross-section strategy were compared with the manual field measured values. The cross-sections based on averaged cloud points sensor profile aggregation was revealed to be the most suitable strategy to measure rut depth. Despite the fact that the study was specifically conducted to measure rut depth, the evaluation results show that the methodology can also be useful for other mobile LiDAR point clouds cross-sections applications.

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3D Mapping of Pavement Distresses Using an Unmanned Aerial Vehicle (UAV) System

Cited by 19 publications

References 5 publications

Analysis of Optimal Flight Parameters of Unmanned Aerial Vehicles (UAVs) for Detecting Potholes in Pavements

Analysis of Optimal Flight Parameters of Unmanned Aerial Vehicles (UAVs) for Detecting Potholes in Pavements

Calibration of UAV Flight Parameters to Inspect the Deterioration of Heritage Façades Using Orthogonal Arrays

Road Rutting Measurement Using Mobile LiDAR Systems Point Cloud

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