Visual Inspection of Precast Concrete Bridge Using UAS Technologies

Junwon Seo, Euiseok Jeong, James P. Wacker

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper proposes that Unmanned Aerial System (UAS) technologies integrated with image visibility enhancement algorithms and machine learning are an efficient yet supplementary concrete bridge inspection tool. Two different image enhancement algorithms, i.e., denoise algorithm and image property adjustment, were considered in this study. To assess the adequacy of the proposed UAS technologies in the bridge inspections, the technologies were applied to identify and quantify defects on an existing concrete double-tee bridge located in the state of South Dakota using a Matrice 210 unit. During the inspections, Matrice 210 recorded videos to extract numerous UAS inspection images throughout the bridge. Machine learning was applied to categorize each of the UAS inspection images into certain defect types such as rust and spalling. The denoise algorithm was used to reduce the noise on the categorized defect images based on the pretrained denoising neural network, while the image property adjustment algorithm was employed to improve the visibility of the images by filtering the images' brightness, contrast, and sharpness. Through these algorithms, defects on the filtered images initially presented with low visibility, were detected. Furthermore, quantification of the defects was able to be completed using pixel-based image analysis with the filtered images. From the UAS-assisted inspections, concrete spalling and rust on railings of the bridge were observed, detected, and quantified successfully. The quantification of spalling showed only a 6.00% difference compared against the inspection report data provided by the South Dakota Department of Transportation (SDDOT).

Original languageEnglish
Title of host publicationDurability, Service Life, and Long-Term Integrity of Concrete Materials, Bridges, and Structures 2021
EditorsYail J. Kim, Chris P. Pantelides, Xianming Shi
PublisherAmerican Concrete Institute
Number of pages14
ISBN (Electronic)9781641951746
StatePublished - Apr 1 2022
EventDurability, Service Life, and Long-Term Integrity of Concrete Materials, Bridges, and Structures 2021 - ACI Fall Concrete Convention 2021 - Virtual, Online
Duration: Oct 17 2021Oct 21 2021

Publication series

NameAmerican Concrete Institute, ACI Special Publication
ISSN (Print)0193-2527


ConferenceDurability, Service Life, and Long-Term Integrity of Concrete Materials, Bridges, and Structures 2021 - ACI Fall Concrete Convention 2021
CityVirtual, Online

Bibliographical note

Funding Information:
Partial financial support for this research was provided by the United States Department of Agriculture (USDA), Forest Service through Joint Venture Agreement No. 18-JV-11111133-031) and in conjunction with the Forest Products Laboratory (FPL). The assistance and cooperation of Terry Fluit, Highway Superintendent at Lincoln County (SD) Highway Department, is gratefully acknowledged.

Publisher Copyright:
© 2022 American Concrete Institute. All rights reserved.


  • bridge
  • image enhancement
  • image processing
  • inspection
  • machine learning
  • pretrained denoising neural network

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Materials Science (all)


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