Applications of machine learning on detection of trace explosives using multispectral imaging

Wenli Huang; Kyle King

doi:10.1117/12.2593373

1 August 2021 Applications of machine learning on detection of trace explosives using multispectral imaging

Wenli Huang, Kyle King

Proceedings Volume 11843, Applications of Machine Learning 2021; 118430W (2021) https://doi.org/10.1117/12.2593373
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Conference Poster

Abstract

This paper presents the algorithms to detect trace chemicals using a multi-wavelength camera. Multispectral images of the chemical and the background were collected using the Ocean Thin Films SpectroCam. The camera has an integrated motor with 8 filter color wheels and 8 interchangeable custom band pass filters in the spectral range of 200–900 nm. Since chemicals have their unique spectral reflectance, the stack of 8-dimensional image data was obtained and subsequently analyzed to develop algorithms that can uniquely identify the area where a chemical is present. In this study, we primarily used RDX, 1,3,5-Trinitroperhydro-1,3,5-triazine, the explosive component in C4. The aim of this study was to investigate the potential of the multispectral imaging system and the accuracy of the model in determining C4 chemical.

Citation Download Citation

Wenli Huang and Kyle King "Applications of machine learning on detection of trace explosives using multispectral imaging", Proc. SPIE 11843, Applications of Machine Learning 2021, 118430W (1 August 2021); https://doi.org/10.1117/12.2593373

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