Next Article in Journal
An Energy Efficient Technique Using Electric Active Shielding for Capacitive Coupling Intra-Body Communication
Previous Article in Journal
BMRC: A Bitmap-Based Maximum Range Counting Approach for Temporal Data in Sensor Monitoring Networks
Open AccessArticle

Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
2
Department of Civil Engineering, University of Seoul, Seoul 02504, Korea
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(9), 2052; https://doi.org/10.3390/s17092052
Received: 10 August 2017 / Revised: 5 September 2017 / Accepted: 6 September 2017 / Published: 7 September 2017
(This article belongs to the Section Remote Sensors)
Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%. View Full-Text
Keywords: concrete structure; crack identification; digital image processing; structural health monitoring; unmanned aerial vehicle concrete structure; crack identification; digital image processing; structural health monitoring; unmanned aerial vehicle
Show Figures

Figure 1

MDPI and ACS Style

Kim, H.; Lee, J.; Ahn, E.; Cho, S.; Shin, M.; Sim, S.-H. Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing. Sensors 2017, 17, 2052. https://doi.org/10.3390/s17092052

AMA Style

Kim H, Lee J, Ahn E, Cho S, Shin M, Sim S-H. Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing. Sensors. 2017; 17(9):2052. https://doi.org/10.3390/s17092052

Chicago/Turabian Style

Kim, Hyunjun; Lee, Junhwa; Ahn, Eunjong; Cho, Soojin; Shin, Myoungsu; Sim, Sung-Han. 2017. "Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing" Sensors 17, no. 9: 2052. https://doi.org/10.3390/s17092052

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop