Three-Dimensional Surface Reconstruction of Space Targets Using a Terahertz MIMO Linear Array Based on Multilayer Wideband Frequency Interferometry Techniques

Zhang, Ye; Wang, Hongqiang; Zeng, Yifan; Deng, Bin; Qin, Yuliang; Yang, Qi

doi:10.1109/tthz.2021.3067171

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…Since the power of the emission source is high, the reflected radiation can obtain a clear image of the human body. Upon the introduction of the concept of Multiple-Input Multiple-Output (MIMO), SAR imagingbased security inspection systems are also in development, which greatly improves the imaging accuracy and rate [4][5][6][7][8][9]. Different from X-ray, active millimeter-wave security equipment does not cause harm to the human body [10].…”

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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