Automated Internal Defect Identification and Localization Based on a Near-Field SAR Millimeter-Wave Imaging System
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 c...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10872942/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823857110275850240 |
|---|---|
| author | Quoc Cuong Bui Weizhi Lin Qiang Huang Gyung-Su Byun |
| author_facet | Quoc Cuong Bui Weizhi Lin Qiang Huang Gyung-Su Byun |
| author_sort | Quoc Cuong Bui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0eea8cd84ebb4741b85933e0afe28e04 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0eea8cd84ebb4741b85933e0afe28e042025-02-12T00:02:11ZengIEEEIEEE Access2169-35362025-01-0113246982471610.1109/ACCESS.2025.353191310872942Automated Internal Defect Identification and Localization Based on a Near-Field SAR Millimeter-Wave Imaging SystemQuoc Cuong Bui0https://orcid.org/0000-0003-0930-9592Weizhi Lin1https://orcid.org/0000-0002-0410-7820Qiang Huang2https://orcid.org/0000-0001-7826-4792Gyung-Su Byun3https://orcid.org/0000-0002-6505-8663Department of Electrical and Computer Engineering, Inha University, Incheon, South KoreaDaniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USADaniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USADepartment of Electrical and Computer Engineering, Inha University, Incheon, South KoreaFast 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.https://ieeexplore.ieee.org/document/10872942/Internal defect detectionnear-field synthetic aperture radar (SAR) imagingmillimeter-wave (mmWave) radarnon-destructive testing (NDT)defect identification and localization |
| spellingShingle | Quoc Cuong Bui Weizhi Lin Qiang Huang Gyung-Su Byun Automated Internal Defect Identification and Localization Based on a Near-Field SAR Millimeter-Wave Imaging System IEEE Access Internal defect detection near-field synthetic aperture radar (SAR) imaging millimeter-wave (mmWave) radar non-destructive testing (NDT) defect identification and localization |
| title | Automated Internal Defect Identification and Localization Based on a Near-Field SAR Millimeter-Wave Imaging System |
| title_full | Automated Internal Defect Identification and Localization Based on a Near-Field SAR Millimeter-Wave Imaging System |
| title_fullStr | Automated Internal Defect Identification and Localization Based on a Near-Field SAR Millimeter-Wave Imaging System |
| title_full_unstemmed | Automated Internal Defect Identification and Localization Based on a Near-Field SAR Millimeter-Wave Imaging System |
| title_short | Automated Internal Defect Identification and Localization Based on a Near-Field SAR Millimeter-Wave Imaging System |
| title_sort | automated internal defect identification and localization based on a near field sar millimeter wave imaging system |
| topic | Internal defect detection near-field synthetic aperture radar (SAR) imaging millimeter-wave (mmWave) radar non-destructive testing (NDT) defect identification and localization |
| url | https://ieeexplore.ieee.org/document/10872942/ |
| work_keys_str_mv | AT quoccuongbui automatedinternaldefectidentificationandlocalizationbasedonanearfieldsarmillimeterwaveimagingsystem AT weizhilin automatedinternaldefectidentificationandlocalizationbasedonanearfieldsarmillimeterwaveimagingsystem AT qianghuang automatedinternaldefectidentificationandlocalizationbasedonanearfieldsarmillimeterwaveimagingsystem AT gyungsubyun automatedinternaldefectidentificationandlocalizationbasedonanearfieldsarmillimeterwaveimagingsystem |