The DOI matches its Crossref record.
Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Azimi, M., Eslamlou, A., & Pekcan, G. (2020) · Sensors · doi:10.3390/s20102778 · Cited by 769
Rakha, T., & Gorodetsky, A. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.05.002 · Cited by 470
Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique
Kim, B., & Cho, S. (2018) · Sensors · doi:10.3390/s18103452 · Cited by 313
Metadata
- Authors
- Morgenthal, G., & Hallermann, N.
- Venue
- Advances in Structural Engineering
- Publisher
- SAGE Publications
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- 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
- The paper
- 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
Data sources
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.