Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery

• Main Authors: Akcay, Samet, Kundegorski, Mikolaj E., Devereux, Michael, Breckon, Toby P.
• Format: Conference Proceeding
• Language: English
• Subjects: baggage X-ray security; Convolutional neural networks; Feature extraction; image classification; Security; Support vector machines; Training; transfer learning; Visualization; Weapons; X-ray imaging
• ISSN: 2381-8549
• DOI: 10.1109/ICIP.2016.7532519

We consider the use of transfer learning, via the use of deep Convolutional Neural Networks (CNN) for the image classification problem posed within the context of X-ray baggage security screening. The use of a deep multi-layer CNN approach, traditionally requires large amounts of training data, in order to facilitate construction of a complex complete end-to-end feature extraction, representation and classification process. Within the context of X-ray security screening, limited availability of training for particular items of interest can thus pose a problem. To overcome this issue, we employ a transfer learning paradigm such that a pre-trained CNN, primarily trained for generalized image classification tasks where sufficient training data exists, can be specifically optimized as a later secondary process that targets specific this application domain. For the classical handgun detection problem we achieve 98.92% detection accuracy outperforming prior work in the field and furthermore extend our evaluation to a multiple object classification task within this context.