Discrimination of drugs and explosives in cargo inspections byapplyingmachine learningin muon tomography

Zheng Yifan; Zeng Zhi; Zeng Ming; Wang Xuewu; Zhao Ziran

doi:10.11884/HPLPB201830.180062

Volume 30 Issue 8

Aug. 2018

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > High Power Laser and Particle Beams > 2018 > 30(8): 086002

Zheng Yifan, Zeng Zhi, Zeng Ming, et al. Discrimination of drugs and explosives in cargo inspections byapplyingmachine learningin muon tomography[J]. High Power Laser and Particle Beams, 2018, 30: 086002. doi: 10.11884/HPLPB201830.180062

Citation:

Zheng Yifan, Zeng Zhi, Zeng Ming, et al. Discrimination of drugs and explosives in cargo inspections byapplyingmachine learningin muon tomography[J]. High Power Laser and Particle Beams, 2018, 30: 086002. doi: 10.11884/HPLPB201830.180062

Citation:

PDF( 8618 KB)

Discrimination of drugs and explosives in cargo inspections byapplyingmachine learningin muon tomography

doi: 10.11884/HPLPB201830.180062

Zheng Yifan^{1, 2, 3, 4
,},
Zeng Zhi^{1, 2, 3
,
,},
Zeng Ming^{1, 2, 3},
Wang Xuewu^{1, 2, 3},
Zhao Ziran^{1, 2, 3}

1.
National Engineering Laboratory, Beijing 100084, China
2.
Key Laboratory of Particle & Radiation lmaging of Ministry of Education (Tsinghua University), Beijing 100084, China
3.
Department of Engineering Physics, Tsinghua University, Beijing 100084, China
4.
Department of Physics, Royal Institute of Technology, Albanova University Center, Stockholm 10691, Sweden

Funds:

National Natural Science Foundation of China 11035002

More Information

Author Bio:
Zheng Yifan(1993—), female, master candidate, engaged in muon tomography; yfzheng11@126.com
Corresponding author: Zeng Zhi(1978—), male, associate professor; zengzhi@tsinghua.edu.cn
Received Date: 2018-03-05
Rev Recd Date: 2018-04-25
Publish Date: 2018-08-15

Abstract

Abstract

A previously under-explored difficulty in cargo inspections is how to efficiently detect drugs and explosives concealed in large dense metals.Cosmic ray muon tomography is a promising non-destructive imaging technique to solve the problem because muons are naturally generated in the atmosphere and have sufficient energy to completely penetrate large dense containers.In this work it is investigated that to what extent drugs and explosives of a certain size could be discriminated from air background and metals by muon tomography within acceptable measuring time.A Geant4 Monte Carlo simulation is built based on the Tsinghua University MUon Tomography facility (TUMUTY) and a support vector machine (SVM) classifier based on machine learning is trained to differentiate drugs and explosives from air background and metals automatically.For various 20 cm×20 cm×20 cm objects, with 10 min to 30 min measuring time, drugs and explosives could be discriminated from background and metals by muon tomography with an error rate of about 1%.With 1 min, the error rate deteriorates to 12.9%.
- muon tomography,
- scattering density,
- drugs and explosives,
- machine learning

FullText(HTML)

References(11)

References

[1]	Decker S H, Chapman M T. Drug smugglers on drug smuggling: Lessons from the inside[M]. Pennsylvania: Temple University Press, 2008.
[2]	Zentai G. X-ray imaging for homeland security[J]. International Journal of Signal and Imaging Systems Engineering, 2010, 3(1): 13-20. doi: 10.1504/IJSISE.2010.034628
[3]	Kiraly B, Olah L, Csikai J. Neutron-based techniques for detection of explosives and drugs[J]. Radiation Physics and Chemistry, 2001, 61(3/6): 781-784.
[4]	Procureur S. Muon imaging: Principles, technologies and applications[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2018, 878: 169-179.
[5]	Chatzidakis S, Choi C K, Tsoukalas L H. Interaction of cosmic ray muons with spent nuclear fuel dry casks and determination of lower detection limit[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2016, 828: 37-45.
[6]	Rossi B B. High-energy particles[M]. New York: Prentice-Hall, 1965.
[7]	He W, Xiao S, Shuai M, et al. A grey incidence algorithm to detect high-Z material using cosmic ray muons[J]. Journal of Instrumentation, 2017, 12(10): P10019. doi: 10.1088/1748-0221/12/10/P10019
[8]	Blanpied G, Kumar S, Dorroh D, et al. Material discrimination using scattering and stopping of cosmic ray muons and electrons: Differentiating heavier from lighter metals as well as low-atomic weight materials[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2015, 784: 352-358. doi: 10.1016/j.nima.2014.11.027
[9]	Wang X, Zeng M, Zeng Z, et al. The cosmic ray muon tomography facility based on large scale MRPC detectors[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2015, 784: 390-393. doi: 10.1016/j.nima.2015.01.024
[10]	Hagmann C, Lange D, Wright D. Cosmic-ray shower generator (CRY) for Monte Carlo transport codes[C]//Nuclear Science Symposium Conference Record. 2007, 2: 1143-1146.
[11]	Nasrabadi N M. Pattern recognition and machine learning[J]. Journal of Electronic Imaging, 2007, 16: 049901. doi: 10.1117/1.2819119