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Proceedings Paper

Diagnosis of crack damage on structures based on image processing techniques and R-CNN using unmanned aerial vehicle (UAV)
Author(s): Jin-Hwan Lee; Sung-Sik Yoon; In-Ho Kim; Hyung-Jo Jung
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Paper Abstract

In this paper, we developed techniques to identify and quantify the damage (crack) to bridges based on images obtained by the unmanned aerial vehicle (UAV). The scope of the research includes image acquisition using UAV, the classification system of crack based on Deep-learning and algorithms of detection and quantification using improved Image Processing Techniques (IPTs). A conventional crack detection method using only IPTs can be applied marginally according to the image acquisition environment (lights, shadows, etc.), so we proposed the techniques based on Deep-learning to find the crack part in the region of interest (ROI) from the other types of damage or non-crack. After classifying the crack part in the ROI, improved IPTs are applied to the detected regions to quantify cracks at 300 micrometers. Performances of the technique were evaluated through preliminary test and field test. The non-contact bridge damage detection technology using UAV can be applied to the actual bridge inspection field It is expected to have more performance than existing bridge inspection methods.

Paper Details

Date Published: 27 March 2018
PDF: 8 pages
Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 1059811 (27 March 2018); doi: 10.1117/12.2296691
Show Author Affiliations
Jin-Hwan Lee, Korea Advanced Institute of Science and Technology (Korea, Republic of)
Sung-Sik Yoon, Korea Advanced Institute of Science and Technology (Korea, Republic of)
In-Ho Kim, Korea Advanced Institute of Science and Technology (Korea, Republic of)
Hyung-Jo Jung, Korea Advanced Institute of Science and Technology (Korea, Republic of)


Published in SPIE Proceedings Vol. 10598:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018
Hoon Sohn, Editor(s)

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