scholarly journals Method for Tunnel Displacements Calculation Based on Mobile Tunnel Monitoring System

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4407
Author(s):  
Zeyu Yue ◽  
Haili Sun ◽  
Ruofei Zhong ◽  
Liming Du
Keyword(s):  
Cross Section ◽  
Laser Scanning ◽  
Structural Changes ◽  
Detection System ◽  
Automatic Measurement ◽  
Detection Algorithm ◽  
Shield Tunnel ◽  
Cloud Data ◽  
Full Cross Section ◽  
Deformation Amount

Efficient, high-precision, and automatic measurement of tunnel structural changes is the key to ensuring the safe operation of subways. Conventional manual, static, and discrete measurements cannot meet the requirements of rapid and full-section detection in subway construction and operation. Mobile laser scanning technology is the primary method for tunnel detection. Herein, we propose a method to calculate shield tunnel displacements of a full cross-section tunnel. The point cloud data, obtained via a mobile tunnel deformation detection system, were fitted, projected, and interpolated to generate an orthophoto image. Combined with the cumulative characteristics of the tunnel gray gradient, the longitudinal ring seam of the tunnel was identified, while the Canny algorithm and Hough line detection algorithm identified the transverse seam. The symmetrical vertical foot method and cross-section superposition analysis were used to calculate the circumferential and radial displacements, respectively. The proposed displacement calculation method achieves automatic recognition of a ring seam, reduces human–computer interaction, and is fast, intelligent, and accurate. Furthermore, the description of the tunnel deformation location and deformation amount is more quantitative and specific. These results confirm the significance of shield tunnel displacement monitoring based on mobile monitoring systems in tunnel disease monitoring.

Intelligent crack damage detection system in shield tunnel using combination of retinanet and optimal adaptive selection

2021 ◽  
pp. 1-17
Author(s):  
Xin Wen Gao ◽  
ShuaiQing Li ◽  
Bang Yang Jin ◽  
Min Hu ◽  
Wei Ding
Keyword(s):  
Large Scale ◽  
Crack Detection ◽  
Detection System ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Shield Tunnel ◽  
False Detection ◽  
Adaptive Selection ◽  
Detection Algorithms ◽  
Tunnel Crack

With the large-scale construction of urban subways, the detection of tunnel cracks becomes particularly important. Due to the complexity of the tunnel environment, it is difficult for traditional tunnel crack detection algorithms to detect and segment such cracks quickly and accurately. The article presents an optimal adaptive selection model (RetinaNet-AOS) based on deep learning RetinaNet for semantic segmentation on tunnel crack images quickly and accurately. The algorithm uses the ROI merge mask to obtain a minimum detection area of the crack in the field of view. A scorer is designed to measure the effect of ROI region segmentation to achieve optimal results, and further optimized with a multi-dimensional classifier. The algorithm is compared with the standard detection based on RetinaNet algorithm with an optimal adaptive selection based on RetinaNet algorithm for different crack types. The results show that our crack detection algorithm not only addresses interference due to mash cracks, slender cracks, and water stains but also the false detection rate decreases from 25.5–35.5% to about 3.6%. Meanwhile, the experimental results focus on the execution time to be calculated on the algorithm, FCN, PSPNet, UNet. The algorithm gives better performance in terms of time complexity.

Subway Tunnel Cross-Section Surveying Based on Ground 3D Laser Scanning Data

2014 ◽  
Vol 1079-1080 ◽  
pp. 296-299
Author(s):  
Xian Ge Cao ◽  
Jin Ling Yang ◽  
Xiang Lai Meng ◽  
Wei Cheng Zhang
Keyword(s):  
Cross Section ◽  
Laser Scanning ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Subway Tunnel ◽  
Cloud Data ◽  
Technical Requirements ◽  
The Cross ◽  
Tunnel Cross Section ◽  
Working Efficiency

Afterthe construction of subway main structure, in order to realize route adjustmentof alignment and gradient, it needs to survey the cross-section of subwaytunnel. Compared with conventional measuring methods, 3D laser scanning has thecharacteristics of non-contact measurement and can collect space 3D point clouddata with high density, this can improve the working efficiency for the subwaycross-section surveying. Based on the Leica Scanstation 2 scanners this paperanalyzed the 3D laser scanning point cloud data collection procedures and dataprocessing, expounded the subway cross-section surveying method based on pointcloud data; analyzed the feasibility of 3D laser scanning technology in theapplication of tunnel cross-section surveying based on the field validationdata. The results show that the cross-section measured by this method can meetthe technical requirements of route adjustment of alignment and gradient.

scholarly journals Cross-Section Deformation Analysis and Visualization of Shield Tunnel Based on Mobile Tunnel Monitoring System

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1006 ◽  
Author(s):  
Haili Sun ◽  
Shuang Liu ◽  
Ruofei Zhong ◽  
Liming Du
Keyword(s):  
Data Acquisition ◽  
Monitoring System ◽  
Point Cloud ◽  
Laser Scanning ◽  
Local Deformation ◽  
Shield Tunnel ◽  
Deformation Analysis ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Tunnel Monitoring

With the ongoing developments in laser scanning technology, applications for describing tunnel deformation using rich point cloud data have become a significant topic of investigation. This study describes the independently developed a mobile tunnel monitoring system called the second version of Tunnel Scan developed by Capital Normal University (CNU-TS-2) for data acquisition, which has an electric system to control its forward speed and is compatible with various laser scanners such as the Faro and Leica models. A comparison with corresponding data acquired by total station data demonstrates that the data collected by CNU-TS-2 is accurate. Following data acquisition, the overall and local deformation of the tunnel is determined by denoising and 360° deformation analysis of the point cloud data. To enhance the expression of the analysis results, this study proposes an expansion of the tunnel point cloud data into a two-dimensional image via cylindrical projection, followed by an expression of the tunnel deformation through color difference to visualize the deformation. Compared with the three-dimensional modeling method of visualization, this method is easier to implement and facilitates storage. In addition, it is conducive to the performance of comprehensive analysis of problems such as water leakage in the tunnel, thereby achieving the effect of multiple uses for a single image.

scholarly journals AN AUTOMATED ROAD ROUGHNESS DETECTION FROM MOBILE LASER SCANNING DATA

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 91-96 ◽  
Author(s):  
P. Kumar ◽  
E. Angelats
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Detection Algorithm ◽  
Road Surface ◽  
Spatial Density ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Road ◽  
Road Users ◽  
Road Roughness

Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.

scholarly journals Comparison of 3D Point Clouds Obtained by Terrestrial Laser Scanning and Personal Laser Scanning on Forest Inventory Sample Plots

Data ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 103
Author(s):  
Christoph Gollob ◽  
Tim Ritter ◽  
Arne Nothdurft
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Automatic Measurement ◽  
Field Measurements ◽  
Ground Truth ◽  
Point Clouds ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Laser Range Finders

In forest inventory, trees are usually measured using handheld instruments; among the most relevant are calipers, inclinometers, ultrasonic devices, and laser range finders. Traditional forest inventory has been redesigned since modern laser scanner technology became available. Laser scanners generate massive data in the form of 3D point clouds. We have developed a novel methodology to provide estimates of the tree positions, stem diameters, and tree heights from these 3D point clouds. This dataset was made publicly accessible to test new software routines for the automatic measurement of forest trees using laser scanner data. Benchmark studies with performance tests of different algorithms are welcome. The dataset contains co-registered raw 3D point-cloud data collected on 20 forest inventory sample plots in Austria. The data were collected by two different laser scanning systems: (1) A mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK) and (2) a static terrestrial laser scanner (TLS) (Focus3D X330, Faro Technologies Inc., Lake Mary, FL, USA). The data also contain digital terrain models (DTMs), field measurements as reference data (ground-truth), and the output of recent software routines for the automatic tree detection and the automatic stem diameter measurement.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals Effects on Lignin Redistribution in Eucalyptus globulus Fibres Pre-Treated by Steam Explosion: A Microscale Study to Cellulose Accessibility

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 507
Author(s):  
Eduardo Troncoso-Ortega ◽  
Rosario del P. Castillo ◽  
Pablo Reyes-Contreras ◽  
Patricia Castaño-Rivera ◽  
Regis Teixeira Mendonça ◽  
...  
Keyword(s):  
Enzymatic Hydrolysis ◽  
Eucalyptus Globulus ◽  
Steam Explosion ◽  
Laser Scanning ◽  
Structural Changes ◽  
Micro Particles ◽  
Cellulose Accessibility ◽  
Ft Ir ◽  
Enzymatic Accessibility ◽  
Physical And Chemical

The objective of this study was to investigate structural changes and lignin redistribution in Eucalyptus globulus pre-treated by steam explosion under different degrees of severity (S0), in order to evaluate their effect on cellulose accessibility by enzymatic hydrolysis. Approximately 87.7% to 98.5% of original glucans were retained in the pre-treated material. Glucose yields after the enzymatic hydrolysis of pre-treated material improved from 19.4% to 85.1% when S0 was increased from 8.53 to 10.42. One of the main reasons for the increase in glucose yield was the redistribution of lignin as micro-particles were deposited on the surface and interior of the fibre cell wall. This information was confirmed by laser scanning confocal fluorescence and FT-IR imaging; these microscopic techniques show changes in the physical and chemical characteristics of pre-treated fibres. In addition, the results allowed the construction of an explanatory model for microscale understanding of the enzymatic accessibility mechanism in the pre-treated lignocellulose.

scholarly journals BIM Supported Surveying and Imaging Combination for Heritage Conservation

2021 ◽  
Vol 13 (8) ◽  
pp. 1584
Author(s):  
Pedro Martín-Lerones ◽  
David Olmedo ◽  
Ana López-Vidal ◽  
Jaime Gómez-García-Bermejo ◽  
Eduardo Zalama
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
3D Point Clouds ◽  
Conservation Actions ◽  
Heritage Building ◽  
2D Imaging ◽  
Digital Survey ◽  
Level 2

As the basis for analysis and management of heritage assets, 3D laser scanning and photogrammetric 3D reconstruction have been probed as adequate techniques for point cloud data acquisition. The European Directive 2014/24/EU imposes BIM Level 2 for government centrally procured projects as a collaborative process of producing federated discipline-specific models. Although BIM software resources are intensified and increasingly growing, distinct specifications for heritage (H-BIM) are essential to driving particular processes and tools to efficiency shifting from point clouds to meaningful information ready to be exchanged using non-proprietary formats, such as Industry Foundation Classes (IFC). This paper details a procedure for processing enriched 3D point clouds into the REVIT software package due to its worldwide popularity and how closely it integrates with the BIM concept. The procedure will be additionally supported by a tailored plug-in to make high-quality 3D digital survey datasets usable together with 2D imaging, enhancing the capability to depict contextualized important graphical data to properly planning conservation actions. As a practical example, a 2D/3D enhanced combination is worked to accurately include into a BIM project, the length, orientation, and width of a big crack on the walls of the Castle of Torrelobatón (Spain) as a representative heritage building.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

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