Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery

IEEE Transactions on Information Forensics and Security

Volumn 13, Issue 9, 2018, Pages 2203-2215

  Akcay, Samet ^a   Kundegorski, Mikolaj E ^a,b   Willcocks, Chris G ^a   Breckon, Toby P ^a  

^a DURHAM UNIVERSITY   (United Kingdom)

^b WORLD HEALTH ORGANIZATION   (Switzerland)

Author keywords

Deep convolutional neural networks; detection; image classification; transfer learning; X ray baggage security

Indexed keywords

CONVOLUTION; DEEP NEURAL NETWORKS; ERROR DETECTION; IMAGE CLASSIFICATION; NETWORK ARCHITECTURE; NETWORK SECURITY; NEURAL NETWORKS; OBJECT DETECTION; OBJECT RECOGNITION; SUPPORT VECTOR MACHINES; VECTOR SPACES; X RAYS;

CLASSIFICATION TECHNIQUE; COMPARATIVE PERFORMANCE; CONVOLUTIONAL NETWORKS; DEEP CONVOLUTIONAL NEURAL NETWORKS; FEATURE EXTRACTION AND CLASSIFICATION; LARGE AMOUNTS OF DATA; OBJECT CLASSIFICATION; TRANSFER LEARNING;

CLASSIFICATION (OF INFORMATION);

EID: 85042851554     PISSN: 15566013     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIFS.2018.2812196     Document Type: Article

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