Millimeter wave radar system is featured by low power consumption, whose strong penetration, owing to the short wavelength all-weather operation. With the rapid development of modern detection, the need of high-quality fast imaging algorithm urgent for security detection. In this paper ,a three-dimensional imaging algorithm on frequency domain is proposed to visualize the target. In detail, a linear frequency modulated continuous wave (LFMCW) radar with working frequency from 60GHz to 64GHz is used to acquire data on which the target image is reconstructed. The basic principles of linear frequency modulation continuous wave radar are introduced in the beginning, then, the echo signal model of the single input single output (SISO) imaging system and the procedures of imaging algorithm are deduced at length The scanning imaging experiment of the target is carried out to evaluate the proposed algorithm, ending up with fine contour of the target. Besides, the resolution of the actual image is obtained by analyzing the image contour. Finally, theoretical resolution is calculated on condition that center frequency equals 62GHz and aperture equals 20cm in both vertical and horizontal directions. The comparison suggests that the actual performance of radar is consistent with the expected one.
The existing terahertz scanning equipment relies heavily on persons’ experience to identify image object. Because the resolution of the terahertz image is not ideal, the work intensity of the security personnel is very high, easy to get distracted or tired, so that long-term accuracy cannot be guaranteed. Therefore, it is of great necessity to intelligentize terahertz security inspection equipment to reduce manual labor intensity by deep learning technique. In this paper, we develop an automatic detection method of hazardous objects based on improved YOLOv8 for a terahertz security inspection equipment. The method realizes the automatic detection of dangerous objects by the improved YOLOv8 model. Specifically, the method incorporates Context Aggregation Networks into the YOLOv8 model to enhance its capability of feature extraction. To adapt to the low resolution of terahertz images, the neck network of YOLOv8 is designed as BiFPN. Additionally, the original C2f residual module is replaced with the C3 module to reduce model parameters and complexity, decreasing computational demands and increasing detection speed. Finally, EIoU is set as the target for model optimization. The experimental results show that the improved YOLOv8 model achieves a 95.2% mAP0.5 and 79.3% mAP0.5-0.95. The computational power requirement of the model is as low as 7 FLOPs and inference time is as fast as 1.4ms. With lower parameters and computations, the improved YOLOv8 model realizes improved detection accuracy and speed, outperforming current mainstream models including Sparse R-CNN, YOLOv5, and SSD, etc.
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