Deep Learning Based Concealed Object Recognition in Active Millimeter Wave Imaging

In application related to public security check system, passive and active imaging of millimeter wave still faces critical challenges in providing high resolution quality images. Improving the detection, localization, and recognition accuracy of concealed object detection systems is very challenging due to the lack of a dataset of millimeter wave images with good resolution. Although previous studies proposed artificial intelligence-based concealed object recognition systems, improving accuracy remains a critical challenge. Therefore, in this paper, we propose two kinds of training dataset generation methods based on the proposed active millimeter wave imaging (AMWI) approaches presented in our previous work to improve the accuracy of convolutional neural networks (CNN)-based concealed object recognition systems. First, a depth-based training dataset generation method and a distance-based training dataset generation method are proposed for specular images and nonspecular images. Finally, a CNN-based concealed object recognition system is proposed by using generated active millimeter wave images and interferometer active images to improve the recognition accuracy.