Abstract
Automatic material classification is very useful for threat detection with X-ray screening technology. In this paper, we propose the use of machine learning methods to the problem of fine-grained classification of organic matters based on multi-energy transmission images, which has been overlooked by existing methods. The method which we propose consists three main steps: spectrum analysis, feature selection and supervised classification. We show detailed analysis of the relationship between feature dimension and material classification accuracy. Our method can also be used to find optimal X-ray configurations for material classification. We compare the performance of several machine learning models for the fine-grained classification task. For the task of classifying three categories of organic matters, we can obtain the classification accuracy higher than 85% with only X-ray measurements with the dimension of four. In conclusion, the results of our paper provide one promising direction for the automatic identification of organic contraband using multi-energy X-ray imaging techniques.
Sponsored by the National Key Research and Development Program (Project No. 2016YFC0800904).
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This research is Sponsored by the National Key Research and Development Program (Project No. 2016YFC0800904).
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Chang, Q., Li, W., Chen, J. (2019). Application of Machine Learning Methods for Material Classification with Multi-energy X-Ray Transmission Images. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_17
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DOI: https://doi.org/10.1007/978-3-030-24274-9_17
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