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

Abstract

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.

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