Position information attention networks for explosive mobile phone classification

Abstract

There is a growing concern about public security, especially the discovery of explosives hidden in mobile phones during security checks. However, there is almost no public dataset of explosive mobile phones to study this topic. We contribute the first explosive mobile phone benchmark dataset for security screening, named explosive mobile phones x-ray image dataset, which will be publicly available. Note that explosive mobile phone classification is a typical class imbalance task. Although the number of explosive mobile phones is far smaller than the number of normal mobile phones, one missed phone can cause a huge loss of life and property. To accurately identify explosives hidden in mobile phones, we propose a module called position information attention module (PIAM). Benefiting from aggregating the position information encoded in networks along the channel and spatial domains, PIAM highlights informative features of explosives. In addition, PIAM combines with other networks effectively at a low cost, empowering them with the ability to identify important details. Furthermore, in the face of the class imbalance, we propose a sample-oriented coefficient called sample cost with an update rule. Extensive experimental results show that PIAM and sample cost significantly improve the performance of many excellent networks in explosive mobile phone classification.