Material classification in X-ray images based on multi-scale CNN

Abstract

Security X-ray baggage scanners provide images based on the different levels of radiation absorption by different materials. Images captured by such scanners are inspected by a human operator, which can slow down the verification process. To speed up inspection time, computer vision and machine learning methods are increasingly being used. While object recognition has been the subject of a huge number of articles, the problem of material recognition in X-ray images still requires some work to achieve equivalent accuracy. This paper focuses on the problem of discrimination of materials into several classes, such as organic substances or metals, in images obtained from dual-energy X-ray security scanners. We propose a new multi-scale convolutional neural network (CNN) for predicting the material class, in which five different sizes of patches are implemented parallelly to balance the trade-off between the increase in the receptive field and the loss of detail. We analyze some regularization methods and activation functions and their impact on the effectiveness of our architecture. The results were compared with other popular CNN architectures and demonstrate the superiority of our solution.