UAV-aided bridge inspection protocol through machine learning with improved visibility images

Euiseok Jeong, Junwon Seo, P. E.James Wacker

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper aims to introduce a new bridge inspection protocol using Convolutional Neural Network (CNN)-based machine learning in conjunction with improved visibility images acquired by Unmanned Aerial Vehicles (UAVs). With two UAVs, separate inspections following the proposed protocol were initially performed indoor to quantify the damage state of three concrete columns and four Cross-Laminated Timber (CLT) beams. The protocol using the two UAVs was also adopted to inspect an in-service four-span timber bridge in Pipestone, Minnesota in the United States. During damage identification, various types of visually detected damage were classified through CNN-based machine learning. For image visibility improvement, each image with damage was processed with appropriate adjustment of brightness, contrast, and sharpness to identify and measure the damage in an efficient way. The proposed protocol was found to be capable of bridge damage identification and measurement with an average error of 9.12% when compared to the direct measurements.

Original languageEnglish
Article number116791
JournalExpert Systems with Applications
Volume197
DOIs
StatePublished - Jul 1 2022

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 the Pipestone County engineers is gratefully acknowledged.

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 the Pipestone County engineers is gratefully acknowledged.

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • Bridge inspection
  • Computer vision
  • Convolutional Neural Network (CNN)
  • Damage quantification
  • Drone
  • Image processing
  • Machine learning
  • Visibility improvement

ASJC Scopus subject areas

  • Engineering (all)
  • Computer Science Applications
  • Artificial Intelligence

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