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

arXiv: https://arxiv.org/abs/2411.18082 · HTML: https://mlsanity.com/paper/2411.18082/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 47 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 29 of 29)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [RepPoints: Point Set Representation for Object Detection](https://mlsanity.com/paper/1904.11490/) (2019)
- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [PixelLM: Pixel Reasoning with Large Multimodal Model](https://mlsanity.com/paper/2312.02228/) (2023)
- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting](https://mlsanity.com/paper/2306.08259/) (2023)
- [Discrete Cosine Transform Network for Guided Depth Map Super-Resolution](https://mlsanity.com/paper/2104.06977/) (2021)
- [SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents](https://mlsanity.com/paper/2305.13040/) (2023)
- [ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling](https://mlsanity.com/paper/2306.12241/) (2023)
- [SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images](https://mlsanity.com/paper/1901.00303/) (2019)
- [Deep Gradient Projection Networks for Pan-sharpening](https://mlsanity.com/paper/2103.04584/) (2021)
- [Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module](https://mlsanity.com/paper/2004.08656/) (2020)
- [Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark](https://mlsanity.com/paper/2108.07020/) (2021)
- [PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning](https://mlsanity.com/paper/2306.12370/) (2023)
- [Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection](https://mlsanity.com/paper/2108.09917/) (2021)
- [A Systematic Survey of Regularization and Normalization in GANs](https://mlsanity.com/paper/2008.08930/) (2020)
- [ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning](https://mlsanity.com/paper/2311.03721/) (2023)
- [AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs](https://mlsanity.com/paper/2306.05562/) (2023)
- [Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition](https://mlsanity.com/paper/2309.14183/) (2023)
- [Collapsed Inference for Bayesian Deep Learning](https://mlsanity.com/paper/2306.09686/) (2023)
- [ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks](https://mlsanity.com/paper/2410.18687/) (2024)
- [On the impact of using X-ray energy response imagery for object detection via Convolutional Neural Networks](https://mlsanity.com/paper/2108.12505/) (2021)
- [Multi-view X-ray R-CNN](https://mlsanity.com/paper/1810.02344/) (2018)

## Built on by (most cited first, 4 of 4)

- [CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors](https://mlsanity.com/paper/2501.16665/) (2025)
- [DAGNet: A Dual-View Attention-Guided Network for Efficient X-ray Security Inspection](https://mlsanity.com/paper/2502.01710/) (2025)
- [Can a Second-View Image Be a Language? Geometric and Semantic Cross-Modal Reasoning for X-ray Prohibited Item Detection](https://mlsanity.com/paper/2511.18385/) (2025)
- [DERA: Detached Edge-Residual Adaptation for Prohibited item Detection](https://mlsanity.com/paper/2609.12411/) (2026)

## Abstract (arXiv)

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