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

Romero-Chambi, Eduardo; Villarroel-Quezada, Simón; Atencio, Edison; Rivera, Felipe Muñoz-La

doi:10.3390/app10124157

Cited by 36 publications

(38 citation statements)

References 52 publications

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“…In the case of the use of UAVs and image capture, recreational and semi-professional drones may not be prepared to withstand rain, for example. Additionally, light conditions on a sunny or cloudy day may affect the quality of the images, so it is necessary to consider weather conditions when planning flights [55][56][57].…”

Section: Cyber-physical Systemsmentioning

confidence: 99%

Interoperability of Digital Tools for the Monitoring and Control of Construction Projects

et al. 2021

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Monitoring the progress on a construction site during the construction phase is crucial. An inadequate understanding of the project status can lead to mistakes and inappropriate actions, causing delays and increased costs. Monitoring and controlling projects via digital tools would reduce the risk of error and enable timely corrective actions. Although there is currently a wide range of technologies for these purposes, these technologies and interoperability between them are still limited. Because of this, it is important to know the possibilities of integration and interoperability regarding their implementation. This article presents a bibliographic synthesis and interpretation of 30 nonconventional digital tools for monitoring progress in terms of field data capture technologies (FDCT) and communication and collaborative technologies (CT) that are responsible for information processing and management. This research aims to perform an integration and interoperability analysis of technologies to demonstrate their potential for monitoring and controlling construction projects during the execution phase. A network analysis was conducted, and the results suggest that the triad formed by building information modeling (BIM), unmanned aerial vehicles (UAVs) and photogrammetry is an effective tool; the use of this set extends not only to monitoring and control, but also to all phases of a project.

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Section: Cyber-physical Systemsmentioning

confidence: 99%

Interoperability of Digital Tools for the Monitoring and Control of Construction Projects

et al. 2021

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“…Due to flexibility and variety of UAS-based imaging platforms, they are also used to perform surveying and monitoring of roads for estimating traffic conditions and road pavement state [22][23][24]. UAS is also an attractive platform for acquiring imagery for 3D reconstruction and technical inspection of industrial large-size objects [25][26][27].…”

Section: Uas Based Photogrammetric Imagingmentioning

confidence: 99%

3D Reconstruction of a Complex Grid Structure Combining UAS Images and Deep Learning

et al. 2020

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The latest advances in technical characteristics of unmanned aerial systems (UAS) and their onboard sensors opened the way for smart flying vehicles exploiting new application areas and allowing to perform missions seemed to be impossible before. One of these complicated tasks is the 3D reconstruction and monitoring of large-size, complex, grid-like structures as radio or television towers. Although image-based 3D survey contains a lot of visual and geometrical information useful for making preliminary conclusions on construction health, standard photogrammetric processing fails to perform dense and robust 3D reconstruction of complex large-size mesh structures. The main problem of such objects is repeated and self-occlusive similar elements resulting in false feature matching. This paper presents a method developed for an accurate Multi-View Stereo (MVS) dense 3D reconstruction of the Shukhov Radio Tower in Moscow (Russia) based on UAS photogrammetric survey. A key element for the successful image-based 3D reconstruction is the developed WireNetV2 neural network model for robust automatic semantic segmentation of wire structures. The proposed neural network provides high matching quality due to an accurate masking of the tower elements. The main contributions of the paper are: (1) a deep learning WireNetV2 convolutional neural network model that outperforms the state-of-the-art results of semantic segmentation on a dataset containing images of grid structures of complicated topology with repeated elements, holes, self-occlusions, thus providing robust grid structure masking and, as a result, accurate 3D reconstruction, (2) an advanced image-based pipeline aided by a neural network for the accurate 3D reconstruction of the large-size and complex grid structured, evaluated on UAS imagery of Shukhov radio tower in Moscow.

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“…The creation of 3D models requires a set of images that must be obtained, considering vertical and horizontal overlaps; the balance between vertical and horizontal overlap allows obtaining optimal results [18,22]. It will be necessary to set flight parameters to build 3D virtual scenarios that allow for an accurate representation of reality [24,25]. Therefore, it is important to consider some variables that condition the quality of the model obtained and influence the accuracy of the information collected.…”

Section: Data Collection Technologymentioning

confidence: 99%

Use of Unmanned Aerial Vehicles (UAVs) and Photogrammetry to Obtain the International Roughness Index (IRI) on Roads

et al. 2020

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Road inspection and maintenance require a large amount of data collection, where the main limiting factor is the time required to cover long stretches of road, having a negative impact on the optimization of the work. This article aims to identify modern tools for road maintenance and analysis. To carry out the research, recent methodologies are used to guide the work in different stages to adequately justify the processes involved. Using unmanned aerial vehicles (UAVs), cameras, and GPS, three-dimensional virtual models are reconstructed, which are useful for extracting the necessary information since they allow for accurate replication of the captured. In this way, it is possible to obtain longitudinal profiles associated with the road, and with it, the international roughness index (IRI) is calculated, which gives results within 0.1 (m/km) of the certified official results, which shows its potential use and development.

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

Cited by 36 publications

References 52 publications

Interoperability of Digital Tools for the Monitoring and Control of Construction Projects

Interoperability of Digital Tools for the Monitoring and Control of Construction Projects

3D Reconstruction of a Complex Grid Structure Combining UAS Images and Deep Learning

Use of Unmanned Aerial Vehicles (UAVs) and Photogrammetry to Obtain the International Roughness Index (IRI) on Roads

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