Development of Convolutional Neural Network Architecture for Detecting Dangerous Goods for X-ray Aviation Security in Artificial Intelligence

Woong Kim; Chulung Lee

doi:10.1007/978-3-030-85906-0_66

Development of Convolutional Neural Network Architecture for Detecting Dangerous Goods for X-ray Aviation Security in Artificial Intelligence

Woong Kim, Chulung Lee

School of Industrial Management Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Aviation-security X-ray equipment is used to screen objects, while human screeners re-examine baggage and travelers to detect prohibited objects. Artificial Intelligence technology is applied to increase the accuracy in searching guns and knives, considered the most dangerous in X-ray images at baggage and aviation security screening. Artificial intelligence aviation security X-ray detects objects, finds them rapidly, reducing screeners’ labor, thereby providing better service to passengers. In this regard, neural networks based on machine learning have been continuously updated to develop such advanced equipment. In this study, the neural network O-Net is developed to improve object detection. O-Net is developed based on U-Net. The developed O-Net is tested for various neural networks, providing a wide range of experimental results.

Original language	English
Title of host publication	Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings
Editors	Alexandre Dolgui, Alain Bernard, David Lemoine, Gregor von Cieminski, David Romero
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	609-615
Number of pages	7
ISBN (Print)	9783030859053
DOIs	https://doi.org/10.1007/978-3-030-85906-0_66
Publication status	Published - 2021
Event	IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021 - Nantes, France Duration: 2021 Sept 5 → 2021 Sept 9

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	632 IFIP
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021
Country/Territory	France
City	Nantes
Period	21/9/5 → 21/9/9

Bibliographical note

Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.

Keywords

Artificial intelligence
Aviation security
Machine learning
X-ray detection

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications
Information Systems and Management

Access to Document

10.1007/978-3-030-85906-0_66

Cite this

Kim, W., & Lee, C. (2021). Development of Convolutional Neural Network Architecture for Detecting Dangerous Goods for X-ray Aviation Security in Artificial Intelligence. In A. Dolgui, A. Bernard, D. Lemoine, G. von Cieminski, & D. Romero (Eds.), Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings (pp. 609-615). (IFIP Advances in Information and Communication Technology; Vol. 632 IFIP). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85906-0_66

Development of Convolutional Neural Network Architecture for Detecting Dangerous Goods for X-ray Aviation Security in Artificial Intelligence. / Kim, Woong; Lee, Chulung.
Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings. ed. / Alexandre Dolgui; Alain Bernard; David Lemoine; Gregor von Cieminski; David Romero. Springer Science and Business Media Deutschland GmbH, 2021. p. 609-615 (IFIP Advances in Information and Communication Technology; Vol. 632 IFIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, W & Lee, C 2021, Development of Convolutional Neural Network Architecture for Detecting Dangerous Goods for X-ray Aviation Security in Artificial Intelligence. in A Dolgui, A Bernard, D Lemoine, G von Cieminski & D Romero (eds), Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings. IFIP Advances in Information and Communication Technology, vol. 632 IFIP, Springer Science and Business Media Deutschland GmbH, pp. 609-615, IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021, Nantes, France, 21/9/5. https://doi.org/10.1007/978-3-030-85906-0_66

Kim W, Lee C. Development of Convolutional Neural Network Architecture for Detecting Dangerous Goods for X-ray Aviation Security in Artificial Intelligence. In Dolgui A, Bernard A, Lemoine D, von Cieminski G, Romero D, editors, Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 609-615. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-030-85906-0_66

Kim, Woong ; Lee, Chulung. / Development of Convolutional Neural Network Architecture for Detecting Dangerous Goods for X-ray Aviation Security in Artificial Intelligence. Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings. editor / Alexandre Dolgui ; Alain Bernard ; David Lemoine ; Gregor von Cieminski ; David Romero. Springer Science and Business Media Deutschland GmbH, 2021. pp. 609-615 (IFIP Advances in Information and Communication Technology).

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Development of Convolutional Neural Network Architecture for Detecting Dangerous Goods for X-ray Aviation Security in Artificial Intelligence

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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