A UAV for bridge inspection: Visual servoing control law with orientation limits

By Najib Metni, Tarek Hamel. 2007

472 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/464d0a342fa5507e12f69d992a0243fa92c85469

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. George Nikolakopoulos
2. Evsen Yanmaz
3. Samira Hayat
4. Raheeb Muzaffar
5. Christoforos Kanellakis

In Computer Vision and Pattern Recognition

1. George Nikolakopoulos
2. R. Thomas
3. Evsen Yanmaz
4. Samira Hayat
5. Raheeb Muzaffar
6. Christoforos Kanellakis
7. Marc Maguire
8. Sattar Dorafshan

Citations in the corpus

• Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete (2018, background): “Previous literature demonstrates several 32 successful applications of image-based inspections to detect cracks [11, 12], spalls [13, 14], delaminations 33 [14, 15, 16], and corrosion [17] in concrete bridges.”
• Survey on Computer Vision for UAVs: Current Developments and Trends (2017, methodology): “Another novel approach has been presented in [29], where a control scheme utilized computer vision for UAV hovering above 2D targets.”
• Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint (2016, methodology): “age domain, urban [32], [50], [61], [89], [93], [96], [107] as well as rural [47], [92], [102], [105], [106], [178] environments have been the focus of research.”