Crack detection of UAV concrete surface images

D. Han

doi:10.1117/12.2525174

6 September 2019 Crack detection of UAV concrete surface images

D. Han

Proceedings Volume 11139, Applications of Machine Learning; 1113914 (2019) https://doi.org/10.1117/12.2525174
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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.

Citation Download Citation

D. Han "Crack detection of UAV concrete surface images", Proc. SPIE 11139, Applications of Machine Learning, 1113914 (6 September 2019); https://doi.org/10.1117/12.2525174

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