UAV Autonomous Inspection and Crack Detection Towards Building Health Monitoring
CSTR:
Author:
Affiliation:

College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China

Clc Number:

P234;TP242.6

  • Article
  • | |
  • Metrics
  • |
  • Reference [25]
  • |
  • Related [20]
  • |
  • Cited by [0]
  • | |
  • Comments
    Abstract:

    Aiming at the demands of time-sensitive building health monitoring to promote the automation level of surface disease visual inspection, scene information guided UAV inspection mission planning was proposed. Based on the scene’s prior information, two observation modes, parallel observation and envelope observation, were designed for the structural characteristics of the building, which realized the full coverage obstacle avoidance inspection of the individual building in the narrow space as well as the observation of whole building with millimeter resolution. Meanwhile, a series of effective quantitative indexes for the overall evaluation of the inspection quality were put forward. The facade of the building was divided into 3 720 subregions. The surface cracks were identified and classified by a deep residual network. The result shows that 13 wrong subregions and 14 missing subregions reflect the high accuracy of crack identification. The crack skeletons are mapped to the reconstructed 3D model, which provides data support for the integrated expression of crack morphology and building information. This study combines high-precision 3D reconstruction with surface disease recognition, providing a practical observation and analysis method for integrated building health monitoring.

    Reference
    [1] 上海市住房和城乡建设管理委员会, 上海建科集团股份有限公司. 上海市建筑业行业发展报告 [M]. 上海: 上海人民出版社, 2021.
    [2] THACKER S, ADSHEAD D, FAY M, et al. Infrastructure for sustainable development [J]. Nature Sustainability, 2019, 2(4): 324.
    [3] KARBHARI V M, ANSARI F. Structural health monitoring of civil infrastructure systems [M]. Cambridge: Woodhead Publishing Limited, 2009.
    [4] NAPOLITANO R, GLISIC B. Methodology for diagnosing crack patterns in masonry structures using photogrammetry and distinct element modeling [J]. Engineering Structures, 2019, 181: 519.
    [5] XIONG H B, CAO J X, ZHANG F L, et al. Investigation of the SHM-oriented model and dynamic characteristics of a super-tall building [J]. Smart Structures and Systems, 2019, 23(3): 295.
    [6] DAS S, SAHA P, PATRO S K. Vibration-based damage detection techniques used for health monitoring of structures: a review [J]. Journal of Civil Structural Health Monitoring, 2016, 6(3): 477.
    [7] KIM M K, WANG Q, LI H. Non-contact sensing based geometric quality assessment of buildings and civil structures: A review [J]. Automation in Construction, 2019, 100: 163.
    [8] MASRI Y EL , RAKHA T. A scoping review of non-destructive testing (NDT) techniques in building performance diagnostic inspections [J]. Construction and Building Materials, 2020, 265: 120542.
    [9] BAUER E, PAVON E, BARREIRA E, et al. Analysis of building facade defects using infrared thermography: Laboratory studies [J]. Journal of Building Engineering, 2016(6): 93.
    [10] MOSTAFA K, HEGAZY T. Review of image-based analysis and applications in construction [J]. Automation in Construction, 2021, 122: 103516.
    [11] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [C]//3rd International Conference on Learning RepresentationsICLR 2015. San Diego:ICLR, 2015: 1-14.
    [12] SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions [C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE, 2015: 1-9.
    [13] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
    [14] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640.
    [15] HE K, GKIOXARI G, DOLLAR P, et al. Mask R-CNN [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 386.
    [16] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation [C]// Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer Cham, 2015: 234-241.
    [17] KHALLAF R, KHALLAF M. Classification and analysis of deep learning applications in construction: A systematic literature review [J]. Automation in Construction, 2021, 129: 103760.
    [18] DONG C-Z, CATBAS F N. A review of computer vision-based structural health monitoring at local and global levels [J]. Structural Health Monitoring, 2020, 20(2): 692.
    [19] SEO J, DUQUE L, WACKER J. Drone-enabled bridge inspection methodology and application [J]. Automation in Construction, 2018, 94: 112.
    [20] VARBLA S, ELLMANN A, PUUST R. Centimetre-range deformations of built environment revealed by drone-based photogrammetry [J]. Automation in Construction, 2021, 128: 103787.
    [21] ZHAO S Z, KANG F, LI J J, et al. Structural health monitoring and inspection of dams based on UAV photogrammetry with image 3D reconstruction [J]. Automation in Construction, 2021, 130: 103832.
    [22] RAKHA T, GORODETSKY A. Review of unmanned aerial system (uas) applications in the built environment: towards automated building inspection procedures using drones [J]. Automation in Construction, 2018, 93: 252.
    [23] CABREIRA T, BRISOLARA L, FERREIRA P R. Survey on coverage path planning with unmanned aerial vehicles [J]. Drones, 2019, 3(1): 4.
    [24] LORENSEN W E, CLINE H E. Marching cubes: A high resolution 3D surface construction algorithm [J]. ACM SIGGRAPH Computer Graphics, 1987, 21(4): 163.
    [25] ZHANG T Y, SUEN C Y. A fast parallel algorithm for thinning digital patterns [J]. Communications of the ACM, 1984, 27(3): 236.
    Comments
    Comments
    分享到微博
    Submit
Get Citation

LIU Chun, AKBAR Akram, CAI Tianchi. UAV Autonomous Inspection and Crack Detection Towards Building Health Monitoring[J].同济大学学报(自然科学版),2022,50(7):921~932

Copy
Share
Article Metrics
  • Abstract:1355
  • PDF: 1278
  • HTML: 211
  • Cited by: 0
History
  • Received:April 17,2022
  • Online: July 22,2022
Article QR Code