SLX: Similarity Learning for X-Ray Screening and Robust Automated Disassembled Object Detection

Baggage screening is important in security-critical applications in airports for detecting threats, including firearms and parts of them. Existing approaches underperform to recognise prohibited objects that are disassembled, especially when learning from limited data and from images produced by different scanners with multi-view orientations. To address such limitations, in this paper, we develop the Similarity Learning X-ray screening (SLX) model for accurate and robust firearm component detection in cluttered scenes. We evaluate SLX on the X-ray Image Library (XIL) dataset that the UK Government has provided us with, for this research. SLX is based on a contrastive similarity learning approach combined with Out-of-Distribution (OoD) detection/ anomaly detection using a deep discriminative model, ResNet-152, for detecting and classifying forbidden items. The evaluation of SLX on the XIL dataset shows that it is effective, beneficial for detecting firearms and their parts, and outperforms other baseline models, on average, by approximately 12 points in accuracy.