Convolutional Neural Networks for Automatic Threat Detection in Security X-Ray Images

Abstract

In this paper we apply Convolutional Neural Networks (CNNs) to the task of automatic threat detection, specifically conventional explosives, in security X-ray scans of passenger baggage. We present the first results of utilizing CNNs for explosives detection, and introduce a dataset, the Passenger Baggage Object Database (PBOD), which can be used by researchers to develop new threat detection algorithms. Using state-of-the-art CNN models and taking advantage of the properties of the Xray scanner, we achieve reliable detection of threats, with the best model achieving an AUC of the ROC of 0.95. We also explore heatmaps as a visualization of the location of the threat.