Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Convolutional Neural Networks

Abstract

Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through Ports of Entry (POE). A combined hardware/software solution that is portable, non-ionizing, handheld, low cost, and fast would represent a significant contribution towards that goal as existing systems do not fulfil many or all of these requirements. To design such a system, Quantum Ventura partnered with Bodkin Design and Engineering to combine long-wave infrared (LWIR) hyperspectral imaging (HSI) with convolutional neural networks (CNNs), implemented on full precision GPUs and neuromorphic computing modules. Our capability study showed that our system can accurately detect and classify contraband in a variety of situations, including varied backgrounds, temperatures, and purities. With a small size, weight, power and cost (SWaP-C) envelope, neuromorphic computing implementations of CNNs showed promising results, though not as well as full precision results.