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"Automated bridge deck evaluation through UAV derived point cloud" presented at CERI2018 by Siyuan Chen

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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.

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"Automated bridge deck evaluation through UAV derived point cloud" presented at CERI2018 by Siyuan Chen

  1. 1. Workshop CERI, UCD, Dublin Wednesday 29th August 2018
  2. 2. Automated Bridge Deck Evaluation through UAV Derived Point Cloud Siyuan Chen, Linh Truong-Hong, Debra F. Laefer, and Eleni Mangina CERI 2018, 29-30th Aug 2018, Dublin, Ireland This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 642453
  3. 3. Traditional Method TLS Survey Vehicle
  4. 4. UAV Inspection UAV System UAV Survey
  5. 5. Flowchart for Data Processing
  6. 6. Image Acquisition
  7. 7. 3D Reconstruction Structure From Motion (SFM) Images form: Prof. Rob Fergus, Computer science, NYU
  8. 8. Reconstruction Result
  9. 9. Noise Reduction Aerial image Point density distribution Dataset prior to denoising Cleared Dataset
  10. 10. Bridge Deck Extraction k-means Region growing
  11. 11. Condition Map
  12. 12. The TRUSS ITN project (http://trussitn.eu) has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 642453 Thanks for your attention

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