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Durham University

Computer Science


Publication details for Mr Samet Akcay

Akcay, S., Kundegorski, M.E., Devereux, M. & Breckon, T.P. (2016), Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery, 2016 IEEE International Conference on Image Processing. Phoenix, AZ, USA, IEEE, Piscataway, NJ, 1057-1061.

Author(s) from Durham


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.