Automated X-ray Image Analysis for Cargo Security: Critical Review and Future Promise

TL;DR

This paper critically reviews the current state of automated X-ray cargo image analysis, highlighting gaps and proposing future research directions to improve security screening processes.

Contribution

It provides a comprehensive review of existing methods, identifies research gaps, and suggests future directions by borrowing insights from related X-ray imaging domains.

Findings

Identification of key gaps in current X-ray cargo analysis methods

Proposal of future research ideas to address these gaps

Borrowing techniques from baggage X-ray domains for cargo analysis

Abstract

We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.