近场毫米波三维成像与异物检测方法

主动式毫米波阵列3维成像系统是人体安检成像系统的研究热点，该文对主动式毫米波阵列3维系统工作模式、信号模型和成像算法进行了介绍，并将深度学习中的卷积神经网络(CNN)热图检测方法和边框回归检测技术应用于人体安检成像异物检测。研究表明，基于热图的检测方法和基于YOLO的检测方法均可实现异物检测。基于热图的检测方法网络结构简单、易训练，但由于需要遍历整幅待检测图像，运算时间长，且生成的检测框尺寸固定，无法适应异物尺寸变化。基于YOLO的检测算法网络结构复杂、训练耗时长，但该方法在检测速度与检测框精度上优势明显，更利于机场安检等对实时性要求较高的检测应用。 

Near-field Millimeter Wave 3D Imaging and Object Detection Method

Active mm-wave linear-array 3D imaging system has become one of the active research areas in the field of imaging for human security. In this paper, the operating mode, signal model, and imaging algorithm are introduced. Deep learning algorithms, including the Convolutional Neural Network (CNN) with heat map and You Only Look Once (YOLO) network, were used for the object detection of human security image. The results show that the method based on heat map and YOLO can both detect foreign objects. We find that the CNN with heat map has a simple network construction and can be easily trained, but the detection process needs to traverse the whole image, which is relatively time-consuming, and the size of the detection region cannot adapt to the objects. On the contrary, though with a relatively complex construction, YOLO network has advantages in terms of detection efficiency and accuracy. Furthermore, the size of the detection region can adapt to the objects, which is more suitable for the human security imaging application.