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http://dx.doi.org/10.12989/sss.2021.28.1.055

Debonding defect quantification method of building decoration layers via UAV-thermography and deep learning  

Peng, Xiong (Hunan University of Science and Technology)
Zhong, Xingu (Hunan Provincial Key Laboratory of Structures for Wind Resistance and Vibration Control & School of Civil Engineering, Hunan University of Science and Technology)
Chen, Anhua (Hunan University of Science and Technology)
Zhao, Chao (Hunan Provincial Key Laboratory of Structures for Wind Resistance and Vibration Control & School of Civil Engineering, Hunan University of Science and Technology)
Liu, Canlong (Hunan University of Science and Technology)
Chen, Y. Frank (Department of Civil Engineering, Pennsylvania State University)
Publication Information
Smart Structures and Systems / v.28, no.1, 2021 , pp. 55-67 More about this Journal
Abstract
The falling offs of building decorative layers (BDLs) on exterior walls are quite common, especially in Asia, which presents great concerns to human safety and properties. Presently, there is no effective technique to detect the debonding of the exterior finish because debonding are hidden defect. In this study, the debonding defect identification method of building decoration layers via UAV-thermography and deep learning is proposed. Firstly, the temperature field characteristics of debonding defects are tested and analyzed, showing that it is feasible to identify the debonding of BDLs based on UAV. Then, a debonding defect recognition and quantification method combining CenterNet (Point Network) and fuzzy clustering is proposed. Further, the actual area of debonding defect is quantified through the optical imaging principle using the real-time measured distance. Finally, a case study of the old teaching-building inspection is carried out to demonstrate the effectiveness of the proposed method, showing that the proposed model performs well with an accuracy above 90%, which is valuable to the society.
Keywords
building decorative layers; debonding defect; deep learning; infrared thermography; UAV;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Saeed, N., King, N., Said, Z. and Omar, M.A. (2019), "Automatic defects detection in CFRP thermograms, using convolutional neural networks and transfer learning", Infrared Phys. Technol., 102, 103048. https://doi.org/10.1016/j.infrared.2019.103048   DOI
2 Kim, D., Youn, J. and Kim, C. (2016), "Automatic photovoltaic panel area extraction from uav thermal infrared images", J. Korean Soc. Survey. Geodesy Photogram. Cartogr., 34(6), 559-568.   DOI
3 Kim, H., Lee, J., Ahn, E., Cho, S., Shin, M. and Sim, S.H. (2017), "Concrete crack identification using a UAV incorporating hybrid image processing", Sensors, 17(9), 2052. https://doi.org/10.3390/s17092052   DOI
4 Liu, P., Chen, A.Y., Huang, Y.N., Han, J.Y., Lai, J.S., Kang, S.C., Wu, T.H., Wen, M.C. and Tsai, M.H. (2014), "A review of rotorcraft Unmanned Aerial Vehicle (UAV) developments and applications in civil engineering", Smart Struct. Syst., Int. J., 13(6), 1065-1094. http://dx.doi.org/10.12989/sss.2014.13.6.1065   DOI
5 Luo, Q., Gao, B., Woo, W.L. and Yang, Y. (2019), "Temporal and spatial deep learning network for infrared thermal defect detection", NDT & E Int., 108, 102164. https://doi.org/10.1016/j.ndteint.2019.102164   DOI
6 Omar, T. and Nehdi, M.L. (2017), "Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography", Automat. Constr., 83, 360-371. https://doi.org/10.1016/j.autcon.2017.06.024   DOI
7 Pitarma, R., Crisostomo, J. and Pereira, L. (2019), "Detection of wood damages using infrared thermography", Procedia Comput. Sci., 155, 480-486. https://doi.org/10.1016/j.procs.2019.08.067   DOI
8 Ozcan, O. and Ozcan, O. (2021), "Automated UAV based multi-hazard assessment system for bridges crossing seasonal rivers", Smart Struct. Syst., Int. J., 27(1), 35-52. https://doi.org/10.12989/sss.2021.27.1.035   DOI
9 Rocha, J.H.A., Povoas, Y.V. and Santos, C.F. (2019), "Detection of delaminations in sunlight-unexposed concrete elements of bridges using infrared thermography", J. Nondestr. Eval., 38(1), 1-12. https://doi.org/10.1007/s10921-018-0546-5   DOI
10 Sirca Jr, G.F. and Adeli, H. (2018), "Infrared thermography for detecting defects in concrete structures", J. Civil Eng. Manage., 24, 508-515.   DOI
11 Jang, K., An, Y.K., Kim, B. and Cho, S. (2021), "Automated crack evaluation of a high-rise bridge pier using a ring-type climbing robot", Comput.-Aided Civil Infrastruct. Eng., 36, 14-29. https://doi.org/10.1111/mice.12550   DOI
12 Biscarini, C., Catapano, I., Cavalagli, N., Ludeno, G., Pepe, F.A. and Ubertini, F. (2020), "UAV photogrammetry, infrared thermography and GPR for enhancing structural and material degradation evaluation of the Roman masonry bridge of Ponte Lucano in Italy", NDT & E Int., 115, 102287. https://doi.org/10.1016/j.ndteint.2020.102287   DOI
13 Cotic, P., Kolaric, D., Bosiljkov, V.B., Bosiljkov, V. and Jaglicic, Z. (2015), "Determination of the applicability and limits of void and delamination detection in concrete structures using infrared thermography", NDT & E Int., 74, 87-93. https://doi.org/10.1016/j.ndteint.2015.05.003   DOI
14 Gong, X., Yao, Q., Wang, M. and Lin, Y. (2018), "A deep learning approach for oriented electrical equipment detection in thermal images", IEEE Access, 6, 41590-41597. https://doi.org/10.1109/ACCESS.2018.2859048   DOI
15 Zhu, H., Yi, C., Hu, Y. and Liu, Q. (2016), "Study on Detection Conditions for Infrared Thermography Diagnosis of Debonding Defect of Exterior Wall Decoration Layer", Build. Technol. China, 47(2), 172-175.
16 Kang, D., Benipal, S.S., Gopal, D.L. and Cha, Y.J. (2020), "Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning", Automat. Constr., 118, 103291. https://doi.org/10.1016/j.autcon.2020.103291   DOI
17 Li, K., Wang, X., Guo, B., Liu, H., and Yuan, H. (2018), "Dynamic simulation of imaging blurring effect of infrared system under vibration of carrier platform", Infrared Laser Eng. China, 47(09), 83-88. https://doi.org/10.3788/IRLA201847.0904004   DOI
18 Zhang, R., Li, H., Duan, K., You, S., Liu, K., Wang, F. and Hu, Y. (2020), "Automatic detection of earthquake-damaged buildings by integrating UAV oblique photography and infrared thermal imaging", Remote Sens., 12(16), 2621. https://doi.org/10.3390/rs12162621   DOI
19 Morgenthal, G., Hallermann, N., Kersten, J., Taraben, J., Debus, P., Helmrich, M. and Rodehorst, V. (2019), "Framework for automated UAS-based structural condition assessment of bridges", Automat. Constr., 97, 77-95. https://doi.org/10.1016/j.autcon.2018.10.006   DOI
20 Hwang, S., An, Y.K., Yang, J. and Sohn, H. (2020), "Remote inspection of internal delamination in wind turbine blades using continuous line laser scanning thermography", Int. J. Precis. Eng. Manuf.-Green Technol., 1-14. https://doi.org/10.1007/s40684-020-00192-9   DOI
21 Jang, K., Kim, N. and An, Y.K. (2019), "Deep learning-based autonomous concrete crack evaluation through hybrid image scanning", Struct. Health Monitor., 18(5-6), 1722-1737. https://doi.org/10.1177/1475921718821719   DOI
22 Zhong, X., Peng, X., Yan, S., Shen, M. and Zhai, Y. (2018), "Assessment of the feasibility of detecting concrete cracks in images acquired by unmanned aerial vehicles", Automat. Constr., 89, 49-57. https://doi.org/10.1016/j.autcon.2018.01.005   DOI
23 Zhou, X., Wang, D. and Krahenbuhl, P. (2019), "Objects as Points", arXiv:1904.07850.
24 Bang, H.T., Park, S. and Jeon, H. (2020), "Defect identification in composite materials thermography and deep learning techniques", Compos. Struct., 246, 112405. https://doi.org/10.1016/j.compstruct.2020.112405   DOI
25 Cheng, C., Shang, Z. and Shen, Z. (2019), "Bridge deck delamination segmentation based on aerial thermography through regularized grayscale morphological reconstruction and gradient statistics", Infrared Phys. Technol., 98, 240-249. https://doi.org/10.1016/j.infrared.2019.03.018   DOI
26 Wang, X., Hu, F. and Huang, S. (2020), "Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering", J. Commun. China, 41(5), 120-129.
27 Yu, J., Jiang, Y., Wang, Z., Cao, Z. and Huang, T. (2016), "Unitbox: An advanced object detection network", Proceedings of the 24th ACM International Conference on Multimedia, pp. 516-520.
28 Zhang, X., Li, C., Meng, Q., Liu, S., Zhang, Y. and Wang, J. (2018), "Infrared image super resolution by combining compressive sensing and deep learning", Sensors, 18(8), 2587. https://doi.org/10.3390/s18082587   DOI
29 Ellenberg, A., Kontsos, A., Moon, F. and Bartoli, I. (2016), "Bridge deck delamination identification from unmanned aerial vehicle infrared thermography", Automat. Constr., 72, 155-165. https://doi.org/10.1016/j.autcon.2016.08.024   DOI
30 Feng, L. and Wang, H. (2014), "Experimental Study on Inside Defects of Building Exterior Wall Decoration Layer by Infrared Thermal Imaging Method", J. Chongqing Jianzhu Univ. China, 36(2), 57-61.
31 Janssens, O., Van de Walle, R., Loccufier, M. and Van Hoecke, S. (2018), "Deep learning for infrared thermal image based machine health monitoring", IEEE/ASME Transact. Mechatron., 23(1), 151-159. https://doi.org/10.1109/TMECH.2017.2722479   DOI
32 Cheng, C., Shang, Z. and Shen, Z. (2020), "Automatic delamination segmentation for bridge deck based on encoder-decoder deep learning through UAV-based thermography", NDT & E Int., 116, 102341. https://doi.org/10.1016/j.ndteint.2020.102341   DOI
33 Jung, H.J., Lee, J.H., Yoon, S. and Kim, I.H. (2019), "Bridge Inspection and condition assessment using Unmanned Aerial Vehicles (UAVs): Major challenges and solutions from a practical perspective", Smart Struct. Syst., Int. J., 24(5), 669-681. https://doi.org/10.12989/sss.2019.24.5.669   DOI