KEYWORDS: Principal component analysis, Image processing, Statistical analysis, Liquids, Image classification, Feature extraction, 3D modeling, Education and training
The Transportation Security Laboratory (TSL) conducts thorough assessments of explosives detection systems (EDSs), encompassing a wide range of explosive materials and hazardous substances. When necessary, inert simulants are employed, but they undergo a stringent verification process to accurately replicate specific properties of threat materials. Whether developed by the TSL or commercially acquired, simulants must undergo verification testing to ensure they mirror the desired threat properties. Historically, these assessments relied on rudimentary metrics like average density and effective atomic number, lacking insight into structural properties possibly being exploited by machine learning detection algorithms. Initial research focused on expanding the verification process by incorporating texture metrics extracted from computed tomography (CT) imagery aimed at deriving features that machine learning detection algorithms might also be utilizing. Two avenues of analysis were devised; first, we calculated 22 metrics through statistical analysis of pixel-based grayscale data, and second, we utilized a convolutional neural network (CNN) to classify images. Both of these methods were subsequently refined and are reported in this work. We augmented the number of metrics for the statistical analysis from 22 to 112, and within the CNN framework we harnessed the flattened array originating from the fully connected layer as a feature map. In both processes the analysis transitioned from a 2-dimensional to a 3-dimensional approach. We assessed the effectiveness of both procedures by testing them on imagery of 50 various materials, such as powders, liquids, putties, and emulsions, using Linear Discriminant Analysis (LDA) to evaluate their ability to distinguish between different materials. Finally, Principal Component Analysis (PCA) loadings were used to define 2-dimensional tolerance intervals for comparisons with loadings from other materials as a way to enhance the current simulant quality control process, ultimately improving the robustness of simulants.
The Transportation Security Laboratory's Simulant Development Laboratory creates x-ray simulants for explosives and other high-hazard threats. Their patented process employs a MATLAB®-based optimization program, originally designed for all material types, but recently adapted for putty-based formulas. To simplify putty-based simulant development, researchers explored polymers and dopants to mimic both rheological and x-ray properties. Using an isoprene liquid rubber and powder ingredients, three putty formulations were crafted: one black and two white. The parametric model utilizes xray signatures from different putty simulant formulations, covering the x-ray range of putty-based explosives. Users can select a target point in the x-ray space, which the model correlates with specific ingredients and preloaded putties.
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.
The Transportation Security Laboratory (TSL) routinely performs test and evaluation of explosives detection systems using real explosives, and detection algorithms are commonly trained using data acquired using real explosives. However, in some cases, explosives are either too dangerous to handle or otherwise restricted, in which case inert simulants are used. Simulants are developed to mimic the physical properties of explosives and other hazardous materials to eliminate the safety risks associated with testing the real threat. The simulant development process at the TSL involves combining chemicals so that the physical properties of the resulting formulation match the target threat and the formulation remains stable and inert. This process becomes increasingly difficult as the number of targeted physical properties increases. To facilitate simulant development, a MATLAB-based program was developed to generate simulant formulas by optimizing the mass percentage of the ingredients such that, when combined, the mixture exhibits the same physical properties as the selected target. In this study, five powder simulants were optimized, manufactured, and tested, and the measured properties were compared to the theoretical values generated by the simulant development program. The results demonstrated the program’s accuracy at predicting each formulation’s physical properties. The accuracies ranged from 79 to 100 percent, with lower accuracies being influenced by difficulties in predicting the formulation’s packing density. The simulant development tool program is patented under US patents 10,998,087 and 11,114,183.
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