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Article

Development of Image Processing for Crack Detection on Concrete Structures through Terrestrial Laser Scanning Associated with the Octree Structure

1
Department of Civil Engineering, University of Seoul, Seoul 02504, Korea
2
School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Gyeonggi 440-746, Korea
3
Department of Convergence Engineering for Future City, Sungkyunkwan University, Gyeonggi 440-746, Korea
4
Department of Safety Engineering, Incheon National University, Incheon 406-772, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2373; https://doi.org/10.3390/app8122373
Received: 18 October 2018 / Revised: 12 November 2018 / Accepted: 21 November 2018 / Published: 23 November 2018
(This article belongs to the Special Issue LiDAR and Time-of-flight Imaging)
Terrestrial laser scanning (TLS) provides a rapid remote sensing technique to model 3D objects but can also be used to assess the surface condition of structures. In this study, an effective image processing technique is proposed for crack detection on images extracted from the octree structure of TLS data. To efficiently utilize TLS for the surface condition assessment of large structures, a process was constructed to compress the original scanned data based on the octree structure. The point cloud data obtained by TLS was converted into voxel data, and further converted into an octree data structure, which significantly reduced the data size but minimized the loss of resolution to detect cracks on the surface. The compressed data was then used to detect cracks on the surface using a combination of image processing algorithms. The crack detection procedure involved the following main steps: (1) classification of an image into three categories (i.e., background, structural joints and sediments, and surface) using K-means clustering according to color similarity, (2) deletion of non-crack parts on the surface using improved subtraction combined with median filtering and K-means clustering results, (3) detection of major crack objects on the surface based on Otsu’s binarization method, and (4) highlighting crack objects by morphological operations. The proposed technique was validated on a spillway wall of a concrete dam structure in South Korea. The scanned data was compressed up to 50% of the original scanned data, while showing good performance in detecting cracks with various shapes. View Full-Text
Keywords: terrestrial laser scanning; 3D scan data; octree data structure; crack detection; image processing; Otsu’s method; K-means clustering terrestrial laser scanning; 3D scan data; octree data structure; crack detection; image processing; Otsu’s method; K-means clustering
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MDPI and ACS Style

Cho, S.; Park, S.; Cha, G.; Oh, T. Development of Image Processing for Crack Detection on Concrete Structures through Terrestrial Laser Scanning Associated with the Octree Structure. Appl. Sci. 2018, 8, 2373. https://doi.org/10.3390/app8122373

AMA Style

Cho S, Park S, Cha G, Oh T. Development of Image Processing for Crack Detection on Concrete Structures through Terrestrial Laser Scanning Associated with the Octree Structure. Applied Sciences. 2018; 8(12):2373. https://doi.org/10.3390/app8122373

Chicago/Turabian Style

Cho, Soojin, Seunghee Park, Gichun Cha, and Taekeun Oh. 2018. "Development of Image Processing for Crack Detection on Concrete Structures through Terrestrial Laser Scanning Associated with the Octree Structure" Applied Sciences 8, no. 12: 2373. https://doi.org/10.3390/app8122373

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