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Cited by 267

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Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures

Morgenthal, G., Hallermann, N.·2014·Advances in Structural Engineering

Abstract

This paper discusses the application of Unmanned Aerial Vehicles (UAV) for visual inspection and damage detection on civil structures. The quality of photos and videos taken by using such airborne vehicles is strongly influenced by numerous parameters such as lighting conditions, distance to the object and vehicle motion induced by environmental effects. Whilst such devices feature highly sophisticated sensors and control algorithms, specifically the effects of fluctuating wind speeds and directions affect the vehicle motion. The nature of vehicle movements during photo and video acquisition in turn affect the quality of the data and hence the degree to which damages can be identified. This paper discusses the properties of such flight systems, the factors influencing their movements and the resulting photo quality. Based on the processed data logged by the high precision sensors on the UAV the influences are studied and a method is shown by which the damage assessment quality may be quantified.

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Metadata

Authors
Morgenthal, G., & Hallermann, N.
Venue
Advances in Structural Engineering
Published
2014-03
Volume
17
Issue
3
Pages
289-302
ISSN
  • 1369-4332 (ISSN-L)
  • 2048-4011
OpenAlex
W2019949051
Cited by
267
References
0
FWCI
15.98
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures. https://app.cochat.ai/papers/10.1260/1369-4332.17.3.289
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Morgenthal, G., & Hallermann, N. (2014). Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures. Advances in Structural Engineering. https://doi.org/10.1260/1369-4332.17.3.289

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.