DCNNs: A Transfer Learning comparison of Full Weapon Family threat detection for Dual-Energy X-Ray Baggage Imagery

TL;DR

This paper develops a transfer learning-based pipeline using CNNs to classify firearm families in Dual-Energy X-Ray baggage images, demonstrating ResNet50's suitability for threat detection at borders.

Contribution

It introduces the first dedicated pipeline for threat classification in Dual-Energy X-Ray imagery and compares CNN architectures for this specific domain.

Findings

ResNet50 outperforms other CNN architectures in accuracy.

Transfer learning significantly improves classification performance.

The pipeline supports operational threat detection needs.

Abstract

Recent advancements in Convolutional Neural Networks have yielded super-human levels of performance in image recognition tasks [13, 25]; however, with increasing volumes of parcels crossing UK borders each year, classification of threats becomes integral to the smooth operation of UK borders. In this work we propose the first pipeline to effectively process Dual-Energy X-Ray scanner output, and perform classification capable of distinguishing between firearm families (Assault Rifle, Revolver, Self-Loading Pistol,Shotgun, and Sub-Machine Gun) from this output. With this pipeline we compare re-cent Convolutional Neural Network architectures against the X-Ray baggage domain via Transfer Learning and show ResNet50 to be most suitable to classification - outlining a number of considerations for operational success within the domain.