Supervised feature learning by adversarial autoencoder approach for object classification in dual X-ray image of luggage

Mohamed Chouai (), Mostefa Merah (), José-Luis Sancho-Gómez () and Malika Mimi ()
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Mohamed Chouai: Mostaganem University
Mostefa Merah: Mostaganem University
José-Luis Sancho-Gómez: Universidad Politécnica de Cartagena
Malika Mimi: Mostaganem University

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 5, No 2, 1112 pages

Abstract: Abstract X-ray inspection by control officers is not always consistent when inspecting baggage since this task are monotonous, tedious and tiring for human inspectors. Thus, a semi-automatic inspection makes sense as a solution in this case. In this perspective, the study presents a novel feature learning model for object classification in luggage dual X-ray images in order to detect explosives objects and firearms. We propose to use supervised feature learning by autoencoders approach. Object detection is performed by a modified YOLOv3 to detect all the presented objects without classification. The features learning is carried out by labeled adversarial autoencoders. The classification is performed by a support vector machine to classify a new object as explosive, firearms or non-threatening objects. To show the superiority of our proposed system, a comparative analysis was carried out to several methods of deep learning. The results indicate that the proposed system leads to efficient objects classification in complex environments, achieving an accuracy of 98.00% and 96.50% in detection of firearms and explosive objects respectively.

Keywords: Transport security; Baggage control; Object detection; YOLOv3; Features learning; Autoencoder (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-019-01498-5

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