A Study on Appearance Checks for Structures with UAV-Based Spatial Imagery Information


  • Dong Moon Kim
  • . .






UAV (Unmanned Aerial Vehicle), Ortho-photos, Aerial photogrammetry, Spatial imagery information, Canny Image Detection technique, 3D spatial information


Background/Objectives: It is difficult to do visual inspections and checks for safety assessment in structures with low accessibility. This study, thus, set out to explore the possibilities of inspecting structure appearances with a UAV(Unmanned Aerial Vehicle)-based image information acquisition system.

Methods/Statistical analysis: Aerial photogrammetry was done with a UAV equipped datum point survey and image photography functions to produce spatial imagery information. The imagery information that was acquired was produced into ortho-photos and compiled as spatial imagery information based on the datum point information. Preprocessing and image analysis (edge extraction) techniques were applied to spatial imagery information to extract cracks in a structure.

Findings: In the survey of ground control points, which have the biggest impacts on the spatial accuracy of spatial imagery information, location accuracy was very good at 0.027m, 0.051m, and 0.106m on the X, Y, and H coordinates, respectively. As an image analysis technique for spatial imagery information, the Canny Image Detection technique was applied to extract 13 risk elements. Compared with on-site visual inspection, it succeeded in detecting 100% of the scaling and wire & cable except for micro-cracks and white rusts, which have no influence on the structural damage of general structures. The geometric forms of cracks detected under the category of cracks were defined with diagonal and perpendicular lines. The cracks were an average of 0.6mm in width and an average of 569mm in length, recording the minimum 301mm and maximum 739mm in length.

Improvements/Applications: The findings show that the UAV-based imagery information acquisition system helped to check and evaluate cracked parts in structures with low accessibility and was expected to serve effectively in the compilation of 3D spatial information as well as the inspection of structural appearance.


[1] Kim, D. (2015). Integration of Near Infrared Image and Probabilistic Classifier to Increase the Classification Accuracy of Point Clouds. Indian Journal of Science and Technology, 8(20), 1-6.

[2] Kim, D., Shin, E. (2014). Vegetation Community Extraction through a Fusion of Multi-Spectral Images and Terrestrial LiDAR Data. International Journal of Applied Engineering Research, 9(22), 14597-14608.

[3] Um, D. Y., Song, Y. H. (2016). 3D Reality Model Creation and Positioning Accuracy Analysis Using Images Obtained with a Low-cost Rotary Wing Drone. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology,6(9), 235-246. doi :http://doi.org/10.14257/AJMAHS.2016.09.57.

[4] Kim, M. K., Jung, K. Y. (2016). Applicability Analysis of Drone Photogrammetry for Updating Digital Maps. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 6(8), 603-610. doi :http://doi.org/10.14257/AJMAHS.2016.08.60.

[5] Chan, B., Guan, H., Jo, J., Blumenstein, M. (2015). Towards UAV-based bridge inspection systems: a review and an application perspective. Structural Monitoring andMaintenance,2(3), 283-300.doi :http://doi.org/10.12989/smm.2015.2.3.283.

[6] Ali, G., Suraj, M., Bal, G.(2016). Prediction of Sewer Pipe Main Condition Using the Linear Regression Approach. Journal of geo science and environment protection, 4(5), 97-104. doi: http://dx.doi.org/10.4236/gep.2016.45010.

[7] Lee, H. B., Kim, J. W., Jang, I. Y. (2012). Development of Automatic Crack Detection System for Concrete Structure Using Image Processing Method. Journal of the Korea institute for structural maintenance and inspection, 16(1), 64-77. doi : http://dx.doi.org/10.11112/jksmi.2012.16.1.064.

[8] Brown, L. G. (1992). A Survey of image Registration Techniques. ACM computing surveys, 24(4), 325-376. doi : http://dx.doi.org/10.1145/146370.146374.

[9] Zitova, B., Flusser, J. (2003). Image registration methods: a survey. Image and Vision Computing, 21(11), 977-1000. doi : http://doi.org/10.1016/S0262-8856(03)00137-9.

[10] Lee, D. G., Yu, Y. G., Ru, J. H., Lee, H. J. (2016). Change Monitoring in Ecological Restoration Area of Open-Pit Mine Using Drone Photogrammetry. Journal of the Korean Society for Geo-spatial Information Science,24(4), 97-104. doi : http://doi.org/10.7319/kogsis.2016.24.4.097.

[11] Snavely, N., Seitz, S, and Szeliski, R. (2008).Modeling the World from Internet Photo Collections. International Journal of Computer Vision, 80(1), 189-210.

[12] Singh S., Chutia, D., and Sudhakar,S.(2012). Development of a Web Based GIS Application for Spatial Natural Resources Information System Using Effective Open Source Software and Standards. Journal of Geographic Information System, 4(3), 261-266.

View Full Article:

How to Cite

Moon Kim, D., & ., . (2018). A Study on Appearance Checks for Structures with UAV-Based Spatial Imagery Information. International Journal of Engineering & Technology, 7(2.33), 1184–1187. https://doi.org/10.14419/ijet.v7i2.33.18013
Received 2018-08-20
Accepted 2018-08-20
Published 2018-06-08