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A high-accuracy hollowness inspection system with sensor fusion of ultra-wide-band radar and depth camera

Published online by Cambridge University Press:  23 December 2022

Haoran Kang
Affiliation:
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, People’s Republic of China
Wentao Zhang
Affiliation:
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, People’s Republic of China
Yangtao Ge
Affiliation:
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, People’s Republic of China
Haiou Liao
Affiliation:
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, People’s Republic of China
Bangzhen Huang
Affiliation:
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, People’s Republic of China
Jing Wu*
Affiliation:
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, People’s Republic of China
Rui-Jun Yan
Affiliation:
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, People’s Republic of China
I-Ming Chen
Affiliation:
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore
*
*Corresponding author. E-mail: wuj@sustech.edu.cn

Abstract

With the dangerous and troublesome nature of hollow defects inside building structures, hollowness inspection has always been a challenge in the field of construction quality assessment. Several methods have been proposed for inspecting hollowness inside concrete structures. These methods have shown great advantages compared to manual inspection but still lack autonomy and have several limitations. In this paper, we propose a range-point migration-based non-contact hollowness inspection system with sensor fusion of ultra-wide-band radar and laser-based depth camera to extract both outer surface and inner hollowness information accurately and efficiently. The simulation result evaluates the performance of the system based on the original range-point migration algorithm, and our proposed one and the result of our system show great competitiveness. Several simulation experiments of structures that are very common in reality are carried out to draw more convincing conclusions about the system. At the same time, a set of laboratory-made concrete components were used as experimental objects for the robotic system. Although still accompanied by some problems, these experiments demonstrate the availability of an automated hollow-core detection system.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Rehman, S. K. U., Ibrahim, Z., Memon, S. A. and Jameel, M., “Nondestructive test methods for concrete bridges: A review,” Constr. Build. Mater. 107, 5886 (2016).CrossRefGoogle Scholar
Cheng, C. and Shen, Z., “Time-series based thermography on concrete block void detection”, (2018).CrossRefGoogle Scholar
Qu, Z., Jiang, P. and Zhang, W., “Development and application of infrared thermography non-destructive testing techniques,” Sensors 20(14), 3851 (2020).CrossRefGoogle ScholarPubMed
Sham, J. F., Lo, T. Y. and Memon, S. A., “Verification and application of continuous surface temperature monitoring technique for investigation of nocturnal sensible heat release characteristics by building fabrics,” Energy Build. 53, 108116 (2012).CrossRefGoogle Scholar
Azari, H., Nazarian, S. and Yuan, D.Assessing sensitivity of impact echo and ultrasonic surface waves methods for nondestructive evaluation of concrete structures,” Constr. Build. Mater. 71, 384391 (2014).CrossRefGoogle Scholar
Chen, D., Montano, V., Huo, L., Fan, S. and Song, G.Detection of subsurface voids in concrete-filled steel tubular (cfst) structure using percussion approach,” Constr. Build. Mater. 262, 119761 (2020).CrossRefGoogle Scholar
Diamanti, N., Giannopoulos, A. and Forde, M. C., “Numerical modelling and experimental verification of gpr to investigate ring separation in brick masonry arch bridges,” NDT E Int. 41(5), 354363 (2008).CrossRefGoogle Scholar
Takahashi, S. and Kidera, S., “Acceleration of range points migration-based microwave imaging for nondestructive testing,” IEEE Antennas Wireless Propag. Lett. 17(4), 702705 (2018).CrossRefGoogle Scholar
Wu, B. and He, L., “Multilayered circular dielectric structure sar imaging based on compressed sensing for FOD detection in NDT,” IEEE Trans. Instrum. Meas. 69(10), 75887593 (2020).CrossRefGoogle Scholar
Biswas, B., Ghatak, R. and Poddar, D., “A fern fractal leaf inspired wideband antipodal vivaldi antenna for microwave imaging system,” IEEE Trans. Antennas Propag. 65(11), 61266129 (2017).CrossRefGoogle Scholar
Dong, Y., Choi, J. and Itoh, T., “Vivaldi antenna with pattern diversity for 0.7 to 2.7 ghz cellular band applications,” IEEE Antennas Wireless Propag. Lett. 17(2), 247250 (2017).CrossRefGoogle Scholar
DeGraaf, S. R.Sar imaging via modern 2-d spectral estimation methods,” IEEE Trans. Image Process. 7(5), 729761 (1998).CrossRefGoogle ScholarPubMed
Akune, K., Kidera, S. and Kirimoto, T., “Fast and Accurate Imaging Algorithm for Targets Buried in Dielectric Medium for UWB Radars,” 2011 URSI General Assembly and Scientific Symposium (IEEE, 2011) pp. 14.CrossRefGoogle Scholar
Büyüköztürk, O., “Imaging of concrete structures,” NDT E Int. 31(4), 233243 (1998).CrossRefGoogle Scholar
Stanley, C. C. and Balendran, R., “Developments in assessing the structural integrity of applied surfaces to concrete buildings,” Structural Survey (1994).CrossRefGoogle Scholar
Yan, R.-J. et al.,Quicabot: Quality inspection and assessment robot,” IEEE Trans. Autom. Sci. Eng. 16(2), 506517 (2018).CrossRefGoogle Scholar
Dvorsky, M., Al Qaseer, M. T. and Zoughi, R., “Crack Sizing Using Dual-Polarized Microwave Sar Imaging,” 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (IEEE, 2020) pp. 16.CrossRefGoogle Scholar
Kidera, S., Sakamoto, T. and Sato, T., “Accurate uwb radar three-dimensional imaging algorithm for a complex boundary without range point connections,” IEEE Trans. Geosci. Remote Sens. 48(4), 19932004 (2010).CrossRefGoogle Scholar
ELsaadouny, M., Barowski, J., Jebramcik, J. and Rolfes, I., “Millimeter Wave Sar Imaging for the Non-destructive Testing of 3d-Printed Samples,” 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA) (IEEE, 2019) pp. 12831285.CrossRefGoogle Scholar
Oka, S., Togo, H., Kukutsu, N. and Nagatsuma, T., “Latest trends in millimeter-wave imaging technology,” Progr. Electromagn. Res. Lett. 1, 197204 (2008).CrossRefGoogle Scholar
Akiyama, Y. and Kidera, S., “Low complexity algorithm for range-point migration-based human body imaging for multistatic uwb radars,” IEEE Geosci. Remote Sens. Lett. 16(2), 216220 (2018).CrossRefGoogle Scholar
Kidera, S. and Kirimoto, T., “Efficient three-dimensional imaging method based on enhanced range point migration for uwb radars,” IEEE Geosci. Remote Sens. Lett. 10(5), 11041108 (2013).CrossRefGoogle Scholar
Li, X., Chen, J. and Zhu, H., “A new method for automated discontinuity trace mapping on rock mass 3d surface model,” Comput. Geosci. 89, 118131 (2016).CrossRefGoogle Scholar
Oskooi, A. F. et al.,Meep: A flexible free-software package for electromagnetic simulations by the fdtd method,” Comput. Phys. Commun. 181(3), 687702 (2010).CrossRefGoogle Scholar