Near-Field Single-Frequency Millimeter-Wave 3-D Imaging via Multifocus Image Fusion

Jing, Handan; Li, Shiyong; Cui, Xiaoxi; Zhao, Guoqiang; Sun, Houjun

doi:10.1109/lawp.2020.3048478

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…17,18 Therefore, only by integrating the images and data of active and passive millimeter wave imaging, can target objects with different shapes and materials be identified more accurately. According to the image feature selection criteria, [19][20][21] the following image feature extraction is studied in this paper.…”

Section: Image Feature Extraction and Object Classification Recognitionmentioning

confidence: 99%

“…Therefore, only by integrating the images and data of active and passive millimeter wave imaging, can target objects with different shapes and materials be identified more accurately. According to the image feature selection criteria, 19–21 the following image feature extraction is studied in this paper. Hu invariant moments: Hu invariant moments are a group of algebraic moments with translational and rotational invariance and scale stability. Eccentricity: Eccentricity describes the compactness of the image as a whole, which also meets the requirements of translation, rotation invariability, and scale stability. Flatness: Flatness describes the narrow length of the target area in the image, which is generally calculated by the ratio of the long axis to the short axis of the target's outer ellipse, which also meets the requirements of translation, rotation invariability, and scale stability. The minimum enclosing rectangle (MEAR): MEAR is the rectangle that can describe the minimum area of the object along the edge of the image. Rectangularity: Rectangularity is the ratio of the target area to the area of the smallest enclosing rectangle. Image gradient: Suppose the 2D discrete image is f (x, y) , then the image gradient is its derivative, that is, the change of pixel value of a point in the x and y directions. …”

Section: Image Feature Extraction and Object Classification Recognitionmentioning

confidence: 99%

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Active and passive mixed millimeter wave imaging target recognition method based on multi‐feature fusion

Zhang

Zong

Qiu

2022

Int J RF Mic Comp-Aid Eng

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Due to millimeter-wave (MMW) has a strong ability to penetrate clothing, MMW holographic imaging technology can conduct a non-contact inspection of the human body's surface. Therefore, it is of great significance to study the security inspection equipment and target recognition technology based on millimeter wave imaging. In this paper, an active and passive hybrid millimeter wave imaging target recognition method based on multi-feature fusion is pro-

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Section: Image Feature Extraction and Object Classification Recognitionmentioning

confidence: 99%

Section: Image Feature Extraction and Object Classification Recognitionmentioning

confidence: 99%

Active and passive mixed millimeter wave imaging target recognition method based on multi‐feature fusion

Zhang

Zong

Qiu

2022

Int J RF Mic Comp-Aid Eng

View full text Add to dashboard Cite

show abstract

“…With the development of millimeter wave imaging technology, the millimeter wave security scheme is becoming more and more sophisticated [1][2][3]. The current milli-meter wave imaging systems can be divided into two categories: passive imaging and active imaging.…”

Section: Introductionmentioning

confidence: 99%

A Suspicious Multi-Object Detection and Recognition Method for Millimeter Wave SAR Security Inspection Images Based on Multi-Path Extraction Network

Yuan

Zhang

et al. 2021

Remote Sensing

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There are several major challenges in detecting and recognizing multiple hidden objects from millimeter wave SAR security inspection images: inconsistent clarity of objects, similar objects, and complex background interference. To address these problems, a suspicious multi-object detection and recognition method based on the Multi-Path Extraction Network (MPEN) is proposed. In MPEN, You Only Look Once (YOLO) v3 is used as the base network, and then the Multi-Path Feature Pyramid (MPFP) module and modified residual block distribution are proposed. MPFP is designed to output the deep network feature layers separately. Then, to distinguish similar objects more easily, the residual block distribution is modified to improve the ability of the shallow network to capture details. To verify the effectiveness of the proposed method, the millimeter wave SAR images from the laboratory’s self-developed security inspection system are utilized in conducting research on multi-object detection and recognition. The detection rate (probability of detecting a target) and average false alarm (probability of error detection) rate of our method on the target are 94.6% and 14.6%, respectively. The mean Average Precision (mAP) of recognizing multi-object is 82.39%. Compared with YOLOv3, our method shows a better performance in detecting and recognizing similar targets.

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“…The algorithm shown in [7] lacks the matched filter to effectively compensate its phase. Jing presented an image fusion method with a single-frequency imaging method [8], but this too cannot obtain the required resolutions. Furthermore, Qiao demonstrated the AMMW 3D scan real-time imaging mechanism by using beam control techniques and fast post-processing algorithms, but the imaging systems need the targets to keep still and hold a special posture while screening, limiting the throughput [9].…”

Section: Introductionmentioning

confidence: 99%

Accurate Imaging of Wide Beam Active Millimeter Wave Based on Angular Spectrum Theory and Simulation Verification

2021

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Due to the fact that a millimeter-wave (MMW) has a strong ability to penetrate clothing, MMW holographic imaging technology can conduct a non-contact inspection of the human body’s surface. In recent years, personnel surveillance systems utilizing MMW holographic imaging technology has achieved rapid progress. However, limited by MMW holographic imaging’s image quality, the existing imaging technology cannot accurately detect whether the human body carries hidden objects. Additionally, real-time inspection requirements cannot be practically satisfied, and the system cost is relatively high. In this paper, a reconstruction algorithm with enhanced imaging quality, which can solve the problem of spherical wave attenuation with distance, making imaging results more accurate. The sampling conditions and imaging resolution are simulated and analyzed, which verify the azimuth resolution. Furthermore, the antenna beam’s holographic imaging simulation is optimized, effectively improving the quality of the reconstructed image. The proposed scheme provides theoretical support for determining antenna step and scanning aperture size in engineering and have theoretical guiding significance for improving the image quality of millimeter-wave holography and reducing system cost.

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Near-Field Single-Frequency Millimeter-Wave 3-D Imaging via Multifocus Image Fusion

Cited by 7 publications

References 26 publications

Active and passive mixed millimeter wave imaging target recognition method based on multi‐feature fusion

Active and passive mixed millimeter wave imaging target recognition method based on multi‐feature fusion

A Suspicious Multi-Object Detection and Recognition Method for Millimeter Wave SAR Security Inspection Images Based on Multi-Path Extraction Network

Accurate Imaging of Wide Beam Active Millimeter Wave Based on Angular Spectrum Theory and Simulation Verification

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