Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?

TL;DR

This paper introduces a large-scale dual-view X-ray dataset and a novel detection framework, AENet, which leverages both views to improve prohibited item detection, mimicking human inspection strategies.

Contribution

The paper presents the LDXray dataset with over 350,000 instances and proposes AENet, a dual-view detection model that enhances performance by emulating human dual-view inspection.

Findings

Dual-view detection significantly improves accuracy, up to 24.7% for umbrellas.

AENet generalizes well across multiple detection models.

The dataset provides a comprehensive resource for training and evaluating dual-view X-ray detection models.

Abstract

To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dual-view X-ray images in the same way humans do? Existing X-ray datasets often suffer from limitations, such as single-view imaging or insufficient sample diversity. To address these gaps, we introduce the Large-scale Dual-view X-ray (LDXray), which consists of 353,646 instances across 12 categories, providing a diverse and comprehensive resource for training and evaluating models. To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet), a novel detection framework that leverages both the main and auxiliary views of the same object. The main-view pipeline focuses on detecting common categories, while the auxiliary-view pipeline handles more challenging categories using ``expert models" learned from the main view. Extensive experiments on the LDXray dataset demonstrate that the dual-view mechanism significantly enhances detection performance, e.g., achieving improvements of up to 24.7% for the challenging category of umbrellas. Furthermore, our results show that AENet exhibits strong generalization across seven different detection models for X-ray Inspection