Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning

TL;DR

This paper introduces a combined terahertz spectroscopy and deep learning system for accurate, stand-off detection and classification of chemicals and explosives, resilient to environmental variations and concealment.

Contribution

It presents a novel integrated THz-TDS and deep learning approach with plasmonic nanoantennas for pixel-level identification of explosives, achieving high accuracy in challenging conditions.

Findings

Achieved 99.42% classification accuracy across eight chemicals.

Maintained 88.83% accuracy for explosives under opaque coverings.

Demonstrated robustness to environmental and sample variations.

Abstract

Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range, conventional terahertz-based detection methods often struggle in real-world environments, where variations in sample geometry, thickness, and packaging can lead to inconsistent spectral responses. In this study, we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays, our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz, supporting practical, stand-off operation. By analyzing individual time-domain pulses with deep neural networks, the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals, including pharmaceutical excipients and explosive compounds, resulted in an average classification accuracy of 99.42% at the pixel level. Notably, the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings, demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.