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 Access content directly
Conference Papers Year : 2021

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

Chulung Lee
  • Function : Author
  • PersonId : 1235652

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.
Fichier principal
Vignette du fichier
520759_1_En_66_Chapter.pdf (369.84 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04022144 , version 1 (09-03-2023)

Licence

Attribution

Identifiers

Cite

Woong Kim, Chulung Lee. Development of Convolutional Neural Network Architecture for Detecting Dangerous Goods for X-ray Aviation Security in Artificial Intelligence. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.609-615, ⟨10.1007/978-3-030-85906-0_66⟩. ⟨hal-04022144⟩
5 View
1 Download

Altmetric

Share

Gmail Facebook X LinkedIn More