scholarly journals Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6669 ◽  
Author(s):  
Hongwei Huang ◽  
Wen Cheng ◽  
Mingliang Zhou ◽  
Jiayao Chen ◽  
Shuai Zhao
Keyword(s):  
Laser Scanning ◽  
Spatial Information ◽  
Point Clouds ◽  
Tunnel Lining ◽  
Shield Tunnel ◽  
Triangle Mesh ◽  
Water Leakage ◽  
Inspection Method ◽  
3D Space ◽  
Low Efficiency

On-site manual inspection of metro tunnel leakages has been faced with the problems of low efficiency and poor accuracy. An automated, high-precision, and robust water leakage inspection method is vital to improve the manual approach. Existing approaches cannot provide the leakage location due to the lack of spatial information. Therefore, an integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning is presented in this paper. It is composed of three parts as follows: (1) establishment of the water leakage dataset using the acquired point clouds of tunnel linings; (2) automated leakage detection via a mask-region-based convolutional neural network; and (3) visualization and quantitative evaluation of the water leakage in 3D space via a novel triangle mesh method. The testing result reveals that the proposed method achieves automated detection and evaluation of tunnel lining water leakages in 3D space, which provides the inspectors with an intuitive overall 3D view of the detected water leakages and the leakage information (area, location, lining segments, etc.).

Innovative digital inspection methodology

2020 ◽  
Vol 60 (1) ◽  
pp. 77
Author(s):  
Stephen A Anderson
Keyword(s):  
Laser Scanning ◽  
Design Parameters ◽  
Destructive Testing ◽  
Inspection Method ◽  
Digital Twin ◽  
3D Space ◽  
Twin Models ◽  
Quality Programs ◽  
Non Destructive ◽  
Digital Record

The paper describes an innovative digital inspection methodology that combines 3D laser scanning, metrology and advanced non-destructive testing data that is merged in 3D space to provide a digital record of the condition and mechanical integrity of critical assets. This advanced inspection method supports condition-based maintenance programs and digital twin models to determine future equipment condition, work scope and inspection schedules, while maintaining a digital record throughout the equipment lifecycle. Testing of the methodology includes 3D scanning of drill platforms, baseline scanning of blowout preventers and sheaves, for quality purposes, and the use of augmented reality for viewing scans. Phased array testing has been conducted on sub-components such as slew ring bolting. Data are combined into digital reports that show 3D images of the equipment with precise dimensional data and identified inspection areas. Such reports can be combined with digital twin models to confirm integrity of the equipment for certificate of conformance and baseline data for future integrity comparisons as equipment ages. This innovative inspection methodology will set a new standard for how equipment data are captured, stored and represented. The process provides a range of benefits for OEMs, drilling contractors and operators alike, including digital quality programs to baseline new equipment condition and compare with design parameters, delivering condition and integrity assessments of critical equipment items in-situ or on deck, providing a consistent methodology for inspection and dimensional control of operational equipment items, and providing precise equipment data that can complement digital twin and real time monitoring programs.

scholarly journals VALIDATING A WORKFLOW FOR TREE INVENTORY UPDATING WITH 3D POINT CLOUDS OBTAINED BY MOBILE LASER SCANNING

2018 ◽  
Vol XLII-2 ◽  
pp. 1163-1168
Author(s):  
J. Wang ◽  
R. Lindenbergh
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Spatial Information ◽  
Open Data ◽  
Point Clouds ◽  
Urban Trees ◽  
Biomass Estimation ◽  
Species Classification ◽  
3D Point Clouds ◽  
Tree Inventory

Urban trees are an important component of our environment and ecosystem. Trees are able to combat climate change, clean the air and cool the streets and city. Tree inventory and monitoring are of great interest for biomass estimation and change monitoring. Conventionally, parameters of trees are manually measured and documented in situ, which is not efficient regarding labour and costs. Light Detection And Ranging (LiDAR) has become a well-established surveying technique for the acquisition of geo-spatial information. Combined with automatic point cloud processing techniques, this in principle enables the efficient extraction of geometric tree parameters. In recent years, studies have investigated to what extend it is possible to perform tree inventories using laser scanning point clouds. Give the availability of a city of Delft Open data tree repository, we are now able to present, validate and extend a workflow to automatically obtain tree data from tree location until tree species. The results of a test over 47 trees show that the proposed methods in the workflow are able to individual urban trees. The tree species classification results based on the extracted tree parameters show that only one tree was wrongly classified using k-means clustering.

scholarly journals SIMULATING VARIOUS TERRESTRIAL AND UAV LIDAR SCANNING CONFIGURATIONS FOR UNDERSTORY FOREST STRUCTURE MODELLING

2017 ◽  
Vol IV-2/W4 ◽  
pp. 59-65 ◽  
Author(s):  
M. Hämmerle ◽  
N. Lukač ◽  
K.-C. Chen ◽  
Zs. Koma ◽  
C.-K. Wang ◽  
...  
Keyword(s):  
Laser Scanning ◽  
3D Structure ◽  
Tree Height ◽  
Point Clouds ◽  
Understory Vegetation ◽  
Forest Plot ◽  
Triangle Mesh ◽  
Vegetation Modelling ◽  
Efficient Planning ◽  
Scanning Strategies

Information about the 3D structure of understory vegetation is of high relevance in forestry research and management (e.g., for complete biomass estimations). However, it has been hardly investigated systematically with state-of-the-art methods such as static terrestrial laser scanning (TLS) or laser scanning from unmanned aerial vehicle platforms (ULS). A prominent challenge for scanning forests is posed by occlusion, calling for proper TLS scan position or ULS flight line configurations in order to achieve an accurate representation of understory vegetation. The aim of our study is to examine the effect of TLS or ULS scanning strategies on (1) the height of individual understory trees and (2) understory canopy height raster models. We simulate full-waveform TLS and ULS point clouds of a virtual forest plot captured from various combinations of max. 12 TLS scan positions or 3 ULS flight lines. The accuracy of the respective datasets is evaluated with reference values given by the virtually scanned 3D triangle mesh tree models. TLS tree height underestimations range up to 1.84 m (15.30 % of tree height) for single TLS scan positions, but combining three scan positions reduces the underestimation to maximum 0.31 m (2.41 %). Combining ULS flight lines also results in improved tree height representation, with a maximum underestimation of 0.24 m (2.15 %). The presented simulation approach offers a complementary source of information for efficient planning of field campaigns aiming at understory vegetation modelling.

scholarly journals POWER LINE EXTRACTION AND ANALYSIS BASED ON LIDAR

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 91-96
Author(s):  
X. H. Chen ◽  
J. Q. Dai ◽  
Y. R. He ◽  
W. W. Ma
Keyword(s):  
Transmission Line ◽  
Point Cloud ◽  
Spatial Information ◽  
Grid Method ◽  
Power Line ◽  
Airborne Lidar ◽  
Practical Significance ◽  
Inspection Method ◽  
Low Efficiency ◽  
Line Point

Abstract. The traditional electrical power line inspection method has the disadvantages of high labor intensity, low efficiency and long cycle of re-inspection. Airborne LiDAR can quickly obtain the high-precision three-dimensional spatial information of transmission line, and the data which collected by it can make it possible to accurately detect the dangerous points.It is proposed to use the grid method to divide the data into multiple regions for the elevation histogram statistical method to obtain the power line point cloud at the complex mountainous terrain. In the non-ground point data, part of the vegetation point cloud is separated according to the point cloud dimension feature, and then the power line point and the pole point are distinguished according to the density characteristics of the point cloud so as to realize the point cloud classification of the transmission line corridor. On this basis, the power line safety distance detection is carried out on the power line points and vegetation points extracted by the classification, and the early warning analysis of the dangerous points of the transmission line tree barrier is completed. The experimental results show that the method can classify the acquired power line corridor point cloud and extract the complete power line, which effectively eliminates the hidden dangers and has certain practical significance.

scholarly journals 3D Object Shape Reconstruction from Underwater Multibeam Data and Over Ground Lidar Scanning

2018 ◽  
Vol 25 (2) ◽  
pp. 47-56 ◽  
Author(s):  
Marek Kulawiak ◽  
Zbigniew Łubniewski
Keyword(s):  
Laser Scanning ◽  
Spatial Information ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Measurement Data ◽  
Point Clouds ◽  
Shape Reconstruction ◽  
Reconstruction Algorithms ◽  
Processing Scheme ◽  
3D Shape Reconstruction

Abstract The technologies of sonar and laser scanning are an efficient and widely used source of spatial information with regards to underwater and over ground environment respectively. The measurement data are usually available in the form of groups of separate points located irregularly in three-dimensional space, known as point clouds. This data model has known disadvantages, therefore in many applications a different form of representation, i.e. 3D surfaces composed of edges and facets, is preferred with respect to the terrain or seabed surface relief as well as various objects shape. In the paper, the authors propose a new approach to 3D shape reconstruction from both multibeam and LiDAR measurements. It is based on a multiple-step and to some extent adaptive process, in which the chosen set and sequence of particular stages may depend on a current type and characteristic features of the processed data. The processing scheme includes: 1) pre-processing which may include noise reduction, rasterization and pre-classification, 2) detection and separation of objects for dedicated processing (e.g. steep walls, masts), and 3) surface reconstruction in 3D by point cloud triangulation and with the aid of several dedicated procedures. The benefits of using the proposed methods, including algorithms for detecting various features and improving the regularity of the data structure, are presented and discussed. Several different shape reconstruction algorithms were tested in combination with the proposed data processing methods and the strengths and weaknesses of each algorithm were highlighted.

scholarly journals GENERATION OF GROUND TRUTH DATASETS FOR THE ANALYSIS OF 3D POINT CLOUDS IN URBAN SCENES ACQUIRED VIA DIFFERENT SENSORS

2018 ◽  
Vol XLII-3 ◽  
pp. 2009-2015 ◽  
Author(s):  
Y. Xu ◽  
Z. Sun ◽  
R. Boerner ◽  
T. Koch ◽  
L. Hoegner ◽  
...  
Keyword(s):  
Point Cloud ◽  
Spatial Information ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Urban Scenes ◽  
3D Space ◽  
3D Point Clouds ◽  
Testing Site ◽  
Voxel Grid

In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

scholarly journals THE ASSESSMENT OF CURVED CENTERLINE GENERATION IN HDMAPS BASED ON POINT CLOUDS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 285-290
Author(s):  
J. C. Zeng ◽  
K. W. Chiang
Keyword(s):  
Laser Scanning ◽  
Road Traffic ◽  
Rapid Development ◽  
Point Clouds ◽  
Horizontal Direction ◽  
Traffic Information ◽  
Large Field ◽  
High Definition ◽  
3D Space ◽  
Lane Line

Abstract. Over the decades, autonomous driving technology has attracted a lot of attention and is under rapid development. However, it still suffers from inadequate accuracy in a certain area, such as the urban area, Global Navigation Satellite System (GNSS) hostile area, due to the multipath interference or Non-Line-of-Sight (NLOS) reception. In order to realize fully autonomous applications, High Definition Maps (HD Maps) become extra assisted information for autonomous vehicles to improve road safety in recent years. Compared with the conventional navigation maps, the accuracy requirement in HD Maps, which is 20 cm in the horizontal direction and 30 cm in 3D space, is considerably higher than the conventional one. Additionally, HD Maps consist of rich and high accurate road traffic information and road elements. For the requirement of high accuracy, conducting a Mobile Laser Scanning (MLS) system is an appropriate method to collect the geospatial data accurately and efficiently. Nowadays, digital vector maps are constructed by digitalizing manually on the collected data. However, the manual process spends a lot of manpower and is not efficient and practical for a large field. Therefore, this paper proposes to automatically construct the crucial road elements, such as road edge, lane line, and centerline, to generate the HD Maps based on point clouds collected by the MMS from the surveying company. The RMSEs in the horizontal direction of the road edge, lane line, and centerline are all lower than 30 cm in 3D space.

Deep learning-based automatic recognition of water leakage area in shield tunnel lining

2020 ◽  
Vol 104 ◽  
pp. 103524 ◽  
Author(s):  
Yadong Xue ◽  
Xinyuan Cai ◽  
Mahdi Shadabfar ◽  
Hua Shao ◽  
Sen Zhang
Keyword(s):  
Deep Learning ◽  
Automatic Recognition ◽  
Tunnel Lining ◽  
Shield Tunnel ◽  
Water Leakage ◽  
Leakage Area

scholarly journals 3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS

2016 ◽  
Vol III-5 ◽  
pp. 105-112 ◽  
Author(s):  
K. Anders ◽  
M. Hämmerle ◽  
G. Miernik ◽  
T. Drews ◽  
A. Escalona ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Point Cloud ◽  
Laser Scanning ◽  
Spatial Information ◽  
Point Clouds ◽  
Rock Surface ◽  
Cloud Data ◽  
Surface Data ◽  
Data Volume

Terrestrial laser scanning constitutes a powerful method in spatial information data acquisition and allows for geological outcrops to be captured with high resolution and accuracy. A crucial aspect for numerous geologic applications is the extraction of rock surface orientations from the data. This paper focuses on the detection of planes in rock surface data by applying a segmentation algorithm directly to a 3D point cloud. Its performance is assessed considering (1) reduced spatial resolution of data and (2) smoothing in the course of data pre-processing. The methodology is tested on simulations of progressively reduced spatial resolution defined by varying point cloud density. Smoothing of the point cloud data is implemented by modifying the neighborhood criteria during normals estima-tion. The considerable alteration of resulting planes emphasizes the influence of smoothing on the plane detection prior to the actual segmentation. Therefore, the parameter needs to be set in accordance with individual purposes and respective scales of studies. Fur-thermore, it is concluded that the quality of segmentation results does not decline even when the data volume is significantly reduced down to 10%. The azimuth and dip values of individual segments are determined for planes fit to the points belonging to one segment. Based on these results, azimuth and dip as well as strike character of the surface planes in the outcrop are assessed. Thereby, this paper contributes to a fully automatic and straightforward workflow for a comprehensive geometric description of outcrops in 3D.

Sign in / Sign up

Export Citation Format

Share Document