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http://dx.doi.org/10.7843/kgs.2018.34.12.145

Crack Detection of Concrete Structure Using Deep Learning and Image Processing Method in Geotechnical Engineering  

Kim, Ah-Ram (Dept. of Infrastructure Safety Research, KICT)
Kim, Donghyeon (NEROPHET Inc.)
Byun, Yo-Seph (Dept. of Infrastructure Safety Research, KICT)
Lee, Seong-Won (Dept. of Infrastructure Safety Research, KICT)
Publication Information
Journal of the Korean Geotechnical Society / v.34, no.12, 2018 , pp. 145-154 More about this Journal
Abstract
The damage investigation and inspection methods performed in concrete facilities such as bridges, tunnels, retaining walls and so on, are usually visually examined by the inspector using the surveying tool in the field. These methods highly depend on the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, the new image processing techniques are necessary in order to automatically detect the cracks and objectively analyze the characteristics of cracks. In this study, deep learning and image processing technique were developed to detect cracks and analyze characteristics in images for concrete facilities. Two-stage image processing pipeline was proposed to obtain crack segmentation and its characteristics. The performance of the method was tested using various crack images with a label and the results showed over 90% of accuracy on crack classification and segmentation. Finally, the crack characteristics (length and thickness) of the crack image pictured from the field were analyzed, and the performance of the developed technique was verified by comparing the actual measured values and errors.
Keywords
Concrete structure; Crack detection; Deep learning; Image processing method;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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