[논문]Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network

Saleem, Muhammad Rakeh; Park, Jong-Woong; Lee, Jin-Hwan; Jung, Hyung-Jo; Sarwar, Muhammad Zohaib

doi:10.1177/1475921720932384

[해외논문] Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network 원문보기

Structural health monitoring, v.20 no.4, 2021년, pp.1760 - 1777

Saleem, Muhammad Rakeh (Department of Civil and Environmental Engineering, Chung-Ang University, Seoul, South Korea) , Park, Jong-Woong (Department of Civil and Environmental Engineering, Chung-Ang University, Seoul, South Korea) , Lee, Jin-Hwan (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea) , Jung, Hyung-Jo (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea) , Sarwar, Muhammad Zohaib (Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, Norway)

Abstract ▼ AI-Helper

The structural condition of bridges is generally assessed using manual visual inspection. However, this approach consumes labor, time, and capital, and produces subjective results. Therefore, industries today are using automated visual inspection approaches, which quantify and localize damages such as cracks using robots and computer vision. This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection. The image capturing and geo-tagging allows the geo-tagging of three-dimensional coordinates and camera pose data with bridge inspection images; the deep convolutional neural network is trained for automated crack identification. The damages extracted by the convolutional neural network are instantly transformed into a global bridge damage map, with georeferencing data acquired using the image capturing and geo-tagging. This method is experimentally validated through a lab-scale test on a wall and a field test on a bridge to demonstrate the performance of the instant damage map.

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