PointNet +  + Based Concealed Object Classification Utilizing an FMCW Millimeter-Wave Radar

Abstract

In the field of millimeter-wave (MMW) imaging, the integration of artificial intelligence (AI) has emerged as a crucial solution for addressing automation challenges. In this study, concealed object classification was successfully achieved on point cloud data from MMW radar high-precision imaging using the PointNet + + deep learning method. The utilized dataset comprises point cloud data generated through the transformation of 3D models and reconstruction of physical objects with an accuracy of less than 1 mm via MMW radar scanning. Classification accuracy was significantly improved by introducing data enhancement techniques, including the generation of homologous data and optimization of sampling points. After several evaluations, 300 epochs of training were conducted using 8192 sampling points, the results showed an accuracy of 0.998 for the training dataset and 0.996 for the test dataset. Moreover, evaluations of samples not included in the original dataset as well as multi-surface scans of concealed objects within the cardboard both resulted in correct predictions, which further validates the effectiveness and reliability of the study and demonstrates the potential of AI applied to MMW imaging.