scholarly journals D3.3 - Millimeter Wave Radar Imaging for Non-Destructive Detection of Material Defects

2017 ◽  
Author(s):  
I. Ullmann ◽  
J. Adametz ◽  
D. Oppelt ◽  
M. Vossiek
Keyword(s):  
Millimeter Wave ◽  
Radar Imaging ◽  
Millimeter Wave Radar ◽  
Material Defects ◽  
Wave Radar ◽  
Non Destructive

scholarly journals Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar

2021 ◽  
Vol 11 (19) ◽  
pp. 8926
Author(s):  
Jie Liu ◽  
Kai Zhang ◽  
Zhenlin Sun ◽  
Qiang Wu ◽  
Wei He ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Imaging System ◽  
Learning Algorithm ◽  
Layer Structure ◽  
Near Field ◽  
Radar Imaging ◽  
Image Resolution ◽  
Sar Images ◽  
Millimeter Wave Radar ◽  
Wave Radar

At present, millimeter wave radar imaging technology has become a recognized human security solution in the field. The millimeter wave radar imaging system can be used to detect a concealed object; multiple-input multiple-output radar antennas and synthetic aperture radar techniques are used to obtain the raw data. The analytical Fourier transform algorithm is used for image reconstruction. When imaging a target at 90 mm from radar, which belongs to the near field imaging scene, the image resolution can reach 1.90 mm in X-direction and 1.73 mm in Y-direction. Since the error caused by the distance between radar and target will lead to noise, the original reconstruction image is processed by gamma transform, which eliminates image noise, then the image is enhanced by linearly stretched transform to improve visual recognition, which lays a good foundation for supervised learning. In order to flexibly deploy the machine learning algorithm in various application scenarios, ShuffleNetV2, MobileNetV3 and GhostNet representative of lightweight convolutional neural networks with redefined convolution, branch structure and optimized network layer structure are used to distinguish multi-category SAR images. Through the fusion of squeeze-and-excitation and the selective kernel attention mechanism, more precise features are extracted for classification, the proposed GhostNet_SEResNet56 can realize the best classification accuracy of SAR images within limited resources, which prediction accuracy is 98.18% and the number of parameters is 0.45 M.

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