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Sensors 2018, 18(6), 1881; https://doi.org/10.3390/s18061881

Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle

1
Applied Science Research Institute, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
2
Department of Civil and Environmental Engineering, Hanbat National University, Daejeon 34158, Korea
3
Division of Electronics and Info-Communication Engineering, YeungJin College, Daegu 41527, Korea
4
School of Architecture, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Korea
5
Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
*
Authors to whom correspondence should be addressed.
Received: 5 March 2018 / Revised: 21 May 2018 / Accepted: 6 June 2018 / Published: 8 June 2018
(This article belongs to the Section Remote Sensors)
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Abstract

Bridge inspection using unmanned aerial vehicles (UAV) with high performance vision sensors has received considerable attention due to its safety and reliability. As bridges become obsolete, the number of bridges that need to be inspected increases, and they require much maintenance cost. Therefore, a bridge inspection method based on UAV with vision sensors is proposed as one of the promising strategies to maintain bridges. In this paper, a crack identification method by using a commercial UAV with a high resolution vision sensor is investigated in an aging concrete bridge. First, a point cloud-based background model is generated in the preliminary flight. Then, cracks on the structural surface are detected with the deep learning algorithm, and their thickness and length are calculated. In the deep learning method, region with convolutional neural networks (R-CNN)-based transfer learning is applied. As a result, a new network for the 384 collected crack images of 256 × 256 pixel resolution is generated from the pre-trained network. A field test is conducted to verify the proposed approach, and the experimental results proved that the UAV-based bridge inspection is effective at identifying and quantifying the cracks on the structures. View Full-Text
Keywords: crack identification; deep learning; unmanned aerial vehicle (UAV); computer vision; spatial information crack identification; deep learning; unmanned aerial vehicle (UAV); computer vision; spatial information
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kim, I.-H.; Jeon, H.; Baek, S.-C.; Hong, W.-H.; Jung, H.-J. Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle. Sensors 2018, 18, 1881.

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