Convolutional Neural Network-Based Terahertz Spectral Classification of Liquid Contraband for Security Inspection

Abstract

Terahertz (THz) spectroscopy is now achieving increasing attention in security inspection owning to its non-destructiveness and deep penetrability of most packaging materials, such as leather, wood and wrapper. However, two major obstacles remain in spectral classification of liquid contraband: the complex components in some contraband and the spectral overlapping effect in similar types of contraband. In this paper, we establish two THz classification datasets and propose a real-time multi-class and multi-concentration liquid contraband spectral classification framework based on a convolutional neural network (CNN). The framework can not only identify contraband with complex components but also classify different concentrations of contraband. We also evaluate the robustness of the framework in different signal-to-noise ratio (SNR). Experimental results demonstrate that our algorithm (CNN) achieves the best performance compared with other deep learning and machine learning algorithms. It can be concluded that THz spectroscopy together with CNN is a promising technique for the classification of liquid contraband.