A Crack Width Measurement Method of UAV Images Using High-Resolution Algorithms

Jonghyeon Yoon, Hyunkyu Shin, Mihwa Song, Heungbae Gil and Sanghyo Lee ()
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Jonghyeon Yoon: Architectural Engineering, Hanyang University ERICA, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Republic of Korea
Hyunkyu Shin: Center for AI Technology in Construction, Hanyang University ERICA, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Republic of Korea
Mihwa Song: ICT Convergence Research Division, Korea Expressway Corporation Research Institute, 24 Dongtansunhwan-daero 17-gil, Dongtan-myeon, Hwaseong 18489, Republic of Korea
Heungbae Gil: ICT Convergence Research Division, Korea Expressway Corporation Research Institute, 24 Dongtansunhwan-daero 17-gil, Dongtan-myeon, Hwaseong 18489, Republic of Korea
Sanghyo Lee: Division of Smart Convergence Engineering, Hanyang University ERICA, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Republic of Korea

Sustainability, 2022, vol. 15, issue 1, 1-11

Abstract: The need for maintenance is increasing due to the aging of facilities. In this study, we proposed a crack width measurement method for images collected at safe distances using UAVs (Unmanned Aerial Vehicles). It is a method of measuring the widths of cracks using a high-resolution (VDSR) algorithm, which measures by increasing the resolution of images taken at 3 m intervals on the wall where cracks exist. In addition, the crack width measurement value was compared with a general photographed image and a high-resolution conversion image. As a result, it was confirmed that the crack width measurement of the image to which the high resolution was applied was similar to the actual measured value. These results can help improve the practical applicability of UAVs for facility safety inspections by overcoming the limits of camera resolution and distances between UAVs and facilities introduced in the facility safety inspection. However, more detailed image resolution is required to quantitatively measure the crack width; we intend to improve this through additional studies.

Keywords: digital image; deep learning; crack width measurement; unmanned aerial vehicle; very deep super-resolution algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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