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

Abstract

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.

Keywords

• near-filed millimeter wave 3d imaging
• back projection
• convolutional neural network (cnn)
• objects detection
• boxing regression