Automatic detection of tunnel lining using image processing supported by terrestrial laser scanning technology

Author(s):  
De-Liang. Chen ◽  
Yan-Yan. Lu
2014 ◽  
Vol 12 ◽  
pp. 41-47 ◽  
Author(s):  
Petr Jašek ◽  
Martin Štroner

Regarding the terrestrial laser scanning accuracy, one of the main problems is the noise in measured distance which is necessary for the spatial coordinates´ determination. In this paper the technique of using the wavelet transformation for the reduction of the noise in the laser scanning data is described. This method of filtration is made in “post processing” and due to this fact any changes in the measuring procedure in the field shouldn´t be done. The creation of the regular matrix is needed to apply image processing. This matrix then makes the range image. In the paper real and simulated efficiency tests of wavelet transformation, the final summary and advantages or disadvantages of this method are introduced.


2018 ◽  
Vol 8 (12) ◽  
pp. 2373 ◽  
Author(s):  
Soojin Cho ◽  
Seunghee Park ◽  
Gichun Cha ◽  
Taekeun Oh

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.


2021 ◽  
Vol 7 (1) ◽  
pp. 51-83
Author(s):  
Davide Tanasi ◽  
Stephan Hassam ◽  
Kaitlyn Kingsland ◽  
Paolo Trapani ◽  
Matthew King ◽  
...  

Abstract The archaeological site of the Domus Romana in Rabat, Malta was excavated almost 100 years ago yielding artefacts from the various phases of the site. The Melite Civitas Romana project was designed to investigate the domus, which may have been the home of a Roman Senator, and its many phases of use. Pending planned archaeological excavations designed to investigate the various phases of the site, a team from the Institute for Digital Exploration from the University of South Florida carried out a digitization campaign in the summer of 2019 using terrestrial laser scanning and aerial digital photogrammetry to document the current state of the site to provide a baseline of documentation and plan the coming excavations. In parallel, structured light scanning and photogrammetry were used to digitize 128 artefacts in the museum of the Domus Romana to aid in off-site research and create a virtual museum platform for global dissemination.


2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.


2021 ◽  
Vol 255 ◽  
pp. 112274
Author(s):  
S. Junttila ◽  
T. Hölttä ◽  
E. Puttonen ◽  
M. Katoh ◽  
M. Vastaranta ◽  
...  

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