Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network
- Authors
- Saleem, Muhammad Rakeh; Park, Jong-Woong; Lee, Jin-Hwan; Jung, Hyung-Jo; Sarwar, Muhammad Zohaib
- Issue Date
- Jul-2021
- Publisher
- SAGE PUBLICATIONS LTD
- Keywords
- Visual inspection; damage detection; image capturing and geo-tagging system; crack map; homography; structural health monitoring
- Citation
- STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, v.20, no.4, pp 1760 - 1777
- Pages
- 18
- Journal Title
- STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
- Volume
- 20
- Number
- 4
- Start Page
- 1760
- End Page
- 1777
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52744
- DOI
- 10.1177/1475921720932384
- ISSN
- 1475-9217
1741-3168
- Abstract
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.