Automated Internal Defect Identification and Localization Based on a Near-Field SAR Millimeter-Wave Imaging System

被引:0
作者
Bui, Quoc Cuong [1 ]
Lin, Weizhi [2 ]
Huang, Qiang [2 ]
Byun, Gyung-Su [1 ]
机构
[1] Inha Univ, Dept Elect & Comp Engn, Incheon 22212, South Korea
[2] Univ Southern Calif, Daniel J Epstein Dept Ind & Syst Engn, Los Angeles, CA 90007 USA
关键词
Millimeter wave communication; Radar imaging; Radar; Radar polarimetry; Location awareness; Accuracy; Defect detection; Synthetic aperture radar; Noise; Imaging; Internal defect detection; near-field synthetic aperture radar (SAR) imaging; millimeter-wave (mmWave) radar; non-destructive testing (NDT); defect identification and localization; GHZ; RESOLUTION; MICROWAVE; FRAMEWORK; IMAGES; RANGE;
D O I
10.1109/ACCESS.2025.3531913
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fast and cost-effective detection of internal defects is essential for structural integrity inspection in various applications such as manufacturing, construction, and aerospace. Current internal non-destructive testing (NDT) methods, such as computed tomography, can be costly, time-consuming, and constrained by object size. The lightweight and affordable millimeter-wave (mmWave) radar has demonstrated the capability of detecting objects beneath surfaces or obstacles through generated near-field synthetic aperture radar (SAR) images. However, its use in precise internal inspection has not been fully explored due to the significant noise in the SAR images and high rate of false identification. To enable accurate and fast inspections using the mmWave radar, this work establishes a robust and automated internal defect detection system. It employs a compact mmWave radar system mounted on a stable rail-based scanning mechanism, generating high-resolution near-field SAR images with an enhanced signal-to-noise ratio through denoising. For fast and accurate detection, an automated defect localization algorithm is developed. The accuracy of detection is ensured by modeling and separating internal defects from disturbances introduced by the scanning mechanism and noise. Experiments were conducted using 3D-printed blocks with synthetic defects to demonstrate the detection capability of the proposed system. Internal defects were accurately detected across variations in shape, material permittivity, depth, and size. The proposed method achieved an average accuracy of 91.7%, outperforming existing methods. The compact design of the radar system enables seamless integration with larger scanning systems, while the automated detection algorithm can be readily implemented as a software module within existing sensing systems. This integration of hardware and software components yields a versatile, low-cost framework for rapid internal health inspection that adapts to various industrial applications and object sizes.
引用
收藏
页码:24698 / 24716
页数:19
相关论文
共 54 条
[1]  
Antkowiak J., 2000, Tech. Rep. COM 9-80-E, V10
[2]   Selection criteria of image reconstruction algorithms for terahertz short-range imaging applications [J].
Barket, Ali Raza ;
Hu, Weidong ;
Wang, Bing ;
Shahzad, Waseem ;
Malik, Jabir Shabbir .
OPTICS EXPRESS, 2022, 30 (13) :23398-23416
[3]   Short-Range SAR Imaging From GHz to THz Waves [J].
Batra, Aman ;
Barowski, Jan ;
Damyanov, Dilyan ;
Wiemeler, Michael ;
Rolfes, Ilona ;
Schultze, Thorsten ;
Balzer, Jan C. ;
Goehringer, Diana ;
Kaiser, Thomas .
IEEE JOURNAL OF MICROWAVES, 2021, 1 (02) :574-585
[4]   Compressed Sensing mm-Wave SAR for Non-Destructive Testing Applications Using Multiple Weighted Side Information [J].
Becquaert, Mathias ;
Cristofani, Edison ;
Huynh Van Luong ;
Vandewal, Marijke ;
Stiens, Johan ;
Deligiannis, Nikos .
SENSORS, 2018, 18 (06)
[5]   Image-Based Surface Defect Detection Using Deep Learning: A Review [J].
Bhatt, Prahar M. ;
Malhan, Rishi K. ;
Rajendran, Pradeep ;
Shah, Brual C. ;
Thakar, Shantanu ;
Yoon, Yeo Jung ;
Gupta, Satyandra K. .
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 2021, 21 (04)
[6]   Simultaneous determination of wave velocity and thickness on overlapped signals using Forward Backward algorithm [J].
Bustillo, J. ;
Achdjian, H. ;
Arciniegas, A. ;
Blanc, L. .
NDT & E INTERNATIONAL, 2017, 86 :100-105
[7]  
Caris M, 2014, EUR MICROW CONF, P1797, DOI 10.1109/EuMC.2014.6986807
[8]  
Dai Huihui, 2023, IET Conference Proceedings, V2023, P4128, DOI 10.1049/icp.2024.1775
[9]  
ELsaadouny M, 2019, PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ELECTROMAGNETICS IN ADVANCED APPLICATIONS (ICEAA), P1283, DOI [10.1109/ICEAA.2019.8879272, 10.1109/iceaa.2019.8879272]
[10]   Visualization and quantitative evaluation of delamination defects in GFRPs via sparse millimeter-wave imaging and image processing [J].
Fang, Yang ;
Chen, Zhenmao ;
Yang, Xihan ;
Wang, Ruonan ;
Li, Yong ;
Xie, Shejuan .
NDT & E INTERNATIONAL, 2024, 141