Visual inspection for illicit items in X-ray images using Deep Learning

TL;DR

This paper provides a comprehensive comparison of deep learning methods for detecting illicit items in X-ray images, highlighting the superiority of Transformer detectors and the efficiency of CSP-DarkNet CNN.

Contribution

It offers the first systematic evaluation of various DNN components for X-ray image analysis under a unified protocol.

Findings

Transformer detectors outperform other models

CSP-DarkNet CNN is highly efficient

Auxiliary neural modules are outdated for this task

Abstract

Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours practically make it a Big Data problem. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage object detectors. However, no comparative experimental assessment of the various relevant DNN components/methods has been performed under a common evaluation protocol, which means that reliable cross-method comparisons are missing. This paper presents exactly such a comparative assessment, utilizing a public relevant dataset and a well-defined methodology for selecting the specific DNN components/modules that are being evaluated. The results indicate the superiority of Transformer detectors, the obsolete nature of auxiliary neural modules that have been developed in the past few years for security applications and the efficiency of the CSP-DarkNet backbone CNN.