Measurement of cracks in concrete bridges by using unmanned aerial vehicles and image registration
File(s)
Published version
Author(s)
Li, Hsuan-Yi
Huang, Chih-Yuan
Wang, Chung-Yue
Type
Journal Article
Abstract
Crack development is a clear indicator of the durability of concrete bridges. Traditional bridge inspections that rely inspectors to climb on bridges with lift cars are unsafe for inspectors and also time- and labor-consuming. Therefore, this research proposes a solution that applies unmanned aerial vehicles (UAV) and high-resolution digital cameras to measure concrete bridge cracks. An experiment was conducted on an Ai-He concrete bridge located in Yangmei District, Taoyuan City, Taiwan. Two types of images were taken. Close-up images observed cracks more clearly, and long-range images covered the ground control points. We registered these two types of images to establish the absolute coordinate system with ground control points and tie points through block triangulation. This research examines three approaches of generating tie points: (1) manually select tie points with features on the bridge such as nails and dots, (2) randomly input tie points generated from Scale-Invariant Feature Transform (SIFT), and (3) randomly input tie points generated from SIFT as the initial tie points and perform automatic tie generation with the ERDAS Leica Photogrammetry Suite (LPS) image matching module (automatic tie generation). Afterwards, close-up images were processed into orthorectified images with 0.1 mm pixel size for crack size measurements. Crack sizes were determined by a manual measurement approach and an inflection point approach for comparison. This research established a workflow for UAV bridge inspection that locates and measures cracks in concrete bridges, which consequently provides a safe and cost-efficient concrete bridge crack monitoring solution with acceptable accuracies.
Date Issued
2023-06
Date Acceptance
2023-05-23
Citation
Drones, 2023, 7 (6)
ISSN
2504-446X
Publisher
MDPI AG
Journal / Book Title
Drones
Volume
7
Issue
6
Copyright Statement
© 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
License URL
Identifier
https://doi.org/10.3390/drones7060342
Publication Status
Published
Article Number
342
Date Publish Online
2023-05-25