Automated Bridge Deck Evaluation through UAV Derived Point Cloud

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Title: Automated Bridge Deck Evaluation through UAV Derived Point Cloud
Authors: Chen, SiyuanTruong-Hong, LinhLaefer, Debra F.Mangina, Eleni
Permanent link: http://hdl.handle.net/10197/10536
Date: 30-Aug-2018
Online since: 2019-05-20T12:25:21Z
Abstract: Imagery-based, three-dimensional (3D) reconstructions from Unmanned Aerial Vehicles (UAVs) hold the potential to provide a safer, more economical, and less disruptive approach for bridge inspection. This paper describes a methodology using a low-cost UAV to generate an imagery-based, dense point cloud for bridge deck inspection. Structure from motion (SfM) is employed to create a three-dimensional (3D) point cloud. Outlier data are removed through a density-based filtering method. Next, the unsupervised learning algorithm k-means and an object-based region growing algorithm are compared for accuracy with respect to bridge deck extraction. Last, an automatic pavement evaluation method is proposed to estimate the deck’s pavement condition. The procedure is demonstrated through an actual case study, in which a 3D point cloud of 16 million valid points was generated from 212 images. With that data set, the region growing method successfully extracted the deck area with an F-score close to 95%, while the unsupervised learning approach only achieved 76%. In the last, to evaluate the surface condition of the extracted pavement, a polynomial surface fitting method was designed to evaluate and visualise the damages.
Funding Details: European Commission
European Commission Horizon 2020
University College Dublin
Type of material: Conference Publication
Publisher: CERAI
Copyright (published version): 2018 the Authors
Keywords: UAVBridge inspectionPoint cloudSegmentationDeck extractionPavement inspectionSfM
Other versions: http://www.cerai.net/
Language: en
Status of Item: Peer reviewed
Is part of: Pakrashi, V., Keenahan, J. (eds.). Civil Engineering Research in Ireland 2018: Conference Proceedings
Conference Details: Civil Engineering Research in Ireland 2018 Conference (CERI 2018), Dublin, Ireland, 29-30 August 2018
ISBN: 978-0-9573957-3-2
Appears in Collections:Computer Science Research Collection

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