|Table of Contents|

Intelligent Inspection Method for Dimensional Quality of Houses Based on 3D Laser Scanning(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2022年04期
Page:
71-80
Research Field:
Publishing date:

Info

Title:
Intelligent Inspection Method for Dimensional Quality of Houses Based on 3D Laser Scanning
Author(s):
LIU Jie-peng1; 2;  CUI Na1; 2;  ZHOU Xu-hong1; 2;  LI Dong-sheng3;  CHENG Guo-zhong1; 2;  ZENG Yan1; 2;  CAO Yu-xing1; 2
(1. School of Civil Engineering, Chongqing University, Chongqing 400045, China; 2. Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, China; 3. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China)
Keywords:
dimensional quality;  intelligent inspection;  3D laser scanning technology;  point cloud data;  reverse modeling
PACS:
TU973.2
DOI:
10.19815/j.jace.2021.07148
Abstract:
An intelligent and full-process method for dimensional quality inspection of houses was proposed based on three dimensional(3D)laser scanning technology, including point cloud data(PCD)registration, PCD simplification, reverse modeling and dimensional quality inspection of houses. PCDs obtained by multi-site were automatically registered through panoramas mapped by PCDs, checkerboard targets detection using YOLO v5 neural network model and target centers estimation using template matching algorithm. In order to accomplish the simplification and accelerate runtime, decomposed graph filtering algorithm was adopted to resample the PCDs. A comprehensive algorithm integrating PCD segmentation, surface reconstruction and dimensional quality inspection was proposed for the registered PCD of houses. The results show that the checkerboard targets-based registration method can register the PCDs of each site automatically and obtain the complete PCD of houses. The PCDs of wall, floor and ceiling can be separated by the PCD segmentation technique and solid model can be built using the surface reconstruction algorithm. Flatness and verticality can be computed by the dimensional quality inspection technique. The proposed intelligent inspection method for the dimensional quality of houses is comprehensive, feasible and applicable to different house types. The research achievements contribute to replace manual measurement to achieve the inspection of flatness and verticality.

References:

[1] 建筑地面工程施工质量验收规范:GB 50209—2010[S].北京:中国计划出版社,2010.
Code of Acceptance of Construction Quality of Building Ground:GB 50209—2010[S].Beijing:China Planning Press,2010.
[2]混凝土结构工程施工质量验收规范:GB 50204—2015[S].北京:中国建筑工业出版社,2015.
Code for Quality Acceptance of Concrete Structure Construction:GB 50204—2015[S].Beijing:China Architecture & Building Press,2015.
[3]PURI N S,VALERO E,TURKAN Y,et al.Assessment of Compliance of Dimensional Tolerances in Concrete Slabs Using TLS Data and the 2D Continuous Wavelet Transform[J].Automation in Construction,2018,94:62-72.
[4]PURI N S,TURKAN Y.A Comparison of TLS-based and ALS-based Techniques for Concrete Floor Waviness Assessment[C]//ISARC.Proceedings of the 31th International Symposium on Automation and Robotics in Construction.Banff:ISARC,2019:1142-1148.
[5]BOSCHE F,GUENET E.Automating Surface Flatness Control Using Terrestrial Laser Scanning and Building Information Models[J].Automation in Construction,2014,44:212-226.
[6]Faro Inc.Focus-3D Technical Specification[M].Lake Mary:Faro Inc,2018.
[7]WANG Q,SOHN H,CHENG J C P.Automatic As-built BIM Creation of Precast Concrete Bridge Deck Panels Using Laser Scan Data[J].Journal of Computing in Civil Engineering,2018,32(3):04018011.
[8]REDMON J,FARHADI A.Yolov3:An Incremental Improvement[J].ArXiv E-prints,2018:1804.02767.
[9]BOCHKOVSKIY A,WANG C Y,LIAO H Y M.Yolov4:Optimal Speed and Accuracy of Object Detection[J].ArXiv,2020:2004.10934.
[10]JOCHER G,STOKEN A,BOROVEC J,et al.Ultralytics/yolov5:v5.0[EB/OL].(2021-04-11)[2021-06-12].https://doi.org/10.5281/zenodo.4679653.
[11]FISCHLER M A,BOLLES R C.Random Sample Consensus:A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography[J].Communications of the ACM,1981,24(6):381-395.
[12]WOLD S,ESBENSEN K,GELADI P.Principal Component Analysis[J].Chemometrics and Intelligent Laboratory Systems,1987,2(1/2/3):37-52.
[13]YIN H,GONG Y H,QIU G P.Side Window Filtering[C]//IEEE.Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:8758-8766.
[14]GOWER J C,DIJKSTERHUIS G B.Procrustes Problems[M].Oxford:Oxford University Press,2004.
[15]CHEN S,TIAN D,FENG C,et al.Fast Resampling of Three-dimensional Point Clouds via Graphs[J].IEEE Transactions on Signal Processing,2018,66(3):666-681.
[16]WU Z,ZENG Y,LI D S,et al.High-volume Point Cloud Data Simplification Based on Decomposed Graph Filtering[J].Automation in Construction,2021,129:103815.
[17]ESTER M,KRIEGEL H P,SANDER J,et al.A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]//SIMOUDIS E.Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining.Portland:AAAI Press,1996:226-231.
[18]SHEWCHUK J R.Delaunay Refinement Algorithms for Triangular Mesh Generation[J].Computational Geometry,2002,22(1/2/3):21-74.
[19]CARR J C,BEATSON R K,CHERRIE J B,et al.Reconstruction and Representation of 3D Objects with Radial Basis Functions[C]//SIGGRAPH.Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques.Los Angeles:ACM Press,2001:67-76.
[20]BERNARDINI F,MITTLEMAN J,RUSHMEIER H,et al.The Ball-pivoting Algorithm for Surface Reconstruction[J].IEEE Transactions on Visualization and Computer Graphics,1999,5(4):349-359.
[21]李晶晶,范大昭,耿弘毅,等.城市点云的区域生长三角网构建方法[J].测绘科学技术学报,2016,33(1):65-70.
LI Jing-jing,FAN Da-zhao,GENG Hong-yi,et al.Triangular Mesh Construction for City Points Based on Region Growing[J].Journal of Geomatics Science and Technology,2016,33(1):65-70.
[22]SUZUKI S,BE K.Topological Structural Analysis of Digitized Binary Images by Border Following[J].Computer Vision,Graphics,and Image Processing,1985,30(1):32-46.
[23]DOUGLAS D H,PEUCKER T K.Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or Its Caricature[J].Cartographica:The International Journal for Geographic Information and Geovisualization,1973,10(2):112-122.
[24]LI D S,LIU J P,FENG L,et al.Terrestrial Laser Scanning Assisted Flatness Quality Assessment for Two Different Types of Concrete Surfaces[J].Measurement,2020,154:107436.

Memo

Memo:
-
Last Update: 2022-07-10