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

Akcay, S. and Kundegorski, M.E. and Devereux, M. and Breckon, T.P. (2016) 'Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery.', in 2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, Arizona, USA ; proceedings. Piscataway, NJ: IEEE, pp. 1057-1061.


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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:03 October 2016
Date deposited:03 October 2016
Date of first online publication:19 August 2016
Date first made open access:No date available

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