Comparison of material classification methods using explosive detection system acquired images

Michael C. Brogden; Patrick J. Cocola; Robert F. Schaffer; Joel B. Rovner; Harry M. Haas; Ronald A. Krauss

doi:10.1117/12.2618789

3 June 2022 Comparison of material classification methods using explosive detection system acquired images

Michael C. Brogden, Patrick J. Cocola, Robert F. Schaffer, Joel B. Rovner, Harry M. Haas, Ronald A. Krauss

Proceedings Volume 12104, Anomaly Detection and Imaging with X-Rays (ADIX) VII; 121040I (2022) https://doi.org/10.1117/12.2618789
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

The Transportation Security Laboratory (TSL) performs testing of explosives detection systems using explosives and other hazardous materials. Inert simulants are also used as substitutes in potentially dangerous testing situations or at testing locations where explosives are prohibited. Each simulant must first be verified that it accurately represents the material on the specific detection platform it was designed for. In addition to the simulant-threat matching, lot-to-lot quality control testing is performed for simulants and threats to ensure that their physical properties remain consistent. Historically, x-ray verification has been limited to using features such as electron density and effective atomic number. While efficient, these features are limited in their application, as they do not provide information related to the material’s structural properties. In this study, four classification methods were tested using imagery-derived texture features to characterize materials and distinguish them from one another. The first three approaches (k-nearest neighbors, support-vector machine, and artificial neural network) were tested using 22 first- and second-order texture features derived from computed tomography images. The fourth method (convolutional neural network) used internally derived features. Based on the test results, a determination was made that the CNN and k-NN were the best algorithms to use to characterize materials based on their texture features.

Conference Presentation

Citation Download Citation

Michael C. Brogden, Patrick J. Cocola, Robert F. Schaffer, Joel B. Rovner, Harry M. Haas, and Ronald A. Krauss "Comparison of material classification methods using explosive detection system acquired images", Proc. SPIE 12104, Anomaly Detection and Imaging with X-Rays (ADIX) VII, 121040I (3 June 2022); https://doi.org/10.1117/12.2618789

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