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Proceedings Paper

Crack detection of UAV concrete surface images
Author(s): D. Han
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Paper Abstract

To improve the robustness of concrete crack detection in complex environments that feature non-uniform illumination, low contrast, and stain noise, such as roads, bridges, I present a systematic approach for automatic crack detection on UAV images for monitoring concrete facilities such as buildings and civil structures. A two-step process was applied. First, a deep learning processing technique for region detection of cracks, and then crack detection based on the image processing and region properties. I applied transfer learning approach to use a pre-trained network in order to identify cracks. I used pixel value based binarization of image data with an edge-preserving filter, which reduced noise in the region. Experimental results from UAV images showed that our approach has a good potential to be applied to concrete crack detection.

Paper Details

Date Published: 6 September 2019
PDF: 9 pages
Proc. SPIE 11139, Applications of Machine Learning, 1113914 (6 September 2019); doi: 10.1117/12.2525174
Show Author Affiliations
D. Han, Chonnam National Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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