Brief history of X-ray security imaging in Computer Vision

[List of datasets and papers (not exhaustive)]

🗃️ Dataset

📈 [2D: 15] [3D: 2]

Name	Type	Year	Class	Prohibited - Negative	Annotations	Views	Open Source
FSOD	2D	2022	20	12,333 - 0	bbox	1	✓ [Link]
EDS	2D	2022	10	14,219 - 0	bbox	1	✓ [Link]
Xray-PI	2D	2022	12	2,409 - 0	bbox, mask	1	✓ [Link]
PIXray	2D	2022	12	5,046 - 0	bbox, mask	1	✓ [Link]
CLCXray	2D	2022	12	9,565 - 0	bbox	1	✓ [Link]
HiXray	2D	2021	8	45,364 - 0	bbox	1	✓ [Link]
deei6	2D	2021	6	7,022 - 0	bbox, mask	2	✕ [Link]
PIDray	2D	2021	12	47,677 - 0	bbox, mask	1	✓ [Link]
AB	2D	2021	--	417 - 6,608	--	2	✕ [Link]
dbf4	2D	2020	4	10,112 - 0	bbox, mask	4	✕ [Link]
OPIXray	2D	2020	5	8,885 - 0	bbox	1	✓ [Link]
SIXray	2D	2019	6	8,929 - 1,050,0302	bbox	1	✓ [Link]
COMPASS-XP	2D	2019	366	1928 - 0	--	1	✓ [Link]
dbf6	2D	2018	6	11,627 - 0	bbox, mask	4	✕ [Link]
GDXray	2D	2015	5	19,407 - 0	bbox	1	✓ [Link]
Dur_3D	3D	2020	5	774 - 0	bbox	--	✕ [Link]
Flitton_3D	3D	2015	2	810 - 2149	bbox	--	✕ [Link]

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📜 Paper

📈 [2D: 141] [3D: 38]

2023

2D

• Optimization and Research of Suspicious Object Detection Algorithm in X-ray Image [Link]
• Object Detection and X-Ray Security Imaging: A Survey [Link]
• RWSC-Fusion: Region-Wise Style-Controlled Fusion Network for the Prohibited X-Ray Security Image Synthesis [Link]
• Transformers for Imbalanced Baggage Threat Recognition [Link]
• CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images [Link]
• Material-Aware Path Aggregation Network and Shape Decoupled SIoU for X-ray Contraband Detection [Link]
• Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening [Link]
• X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection [Link]
• Cascaded structure tensor for robust baggage threat detection [Link]
• Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey [Link]

2022

2D

• Learning-based Material Classification in X-ray Security Images [Link]
• Few-shot X-ray Prohibited Item Detection: A Benchmark and Weak-feature Enhancement Network [Link]
• Balanced Affinity Loss for Highly Imbalanced Baggage Threat Contour-Driven Instance Segmentation [Link]
• Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery [Link]
• Automatic Baggage Threat Detection Using Deep Attention Networks [Link]
• A Multi-Task Semantic Segmentation Network for Threat Detection in X-Ray Security Images [Link]
• Dualray: Dual-View X-ray Security Inspection Benchmark and Fusion Detection Framework [Link]
• MFA-net: Object detection for complex X-ray cargo and baggage security imagery [Link]
• Baggage Threat Recognition Using Deep Low-Rank Broad Learning Detector [Link]
• Exploring Endogenous Shift for Cross-domain Detection: A Large-scale Benchmark and Perturbation Suppression Network [Link]
• Improved YOLOX detection algorithm for contraband in X-ray images [Link]
• LightRay: Lightweight network for prohibited items detection in X-ray images during security inspection [Link]
• Benefits of Decision Support Systems in Relation to Task Difficulty in Airport Security X-Ray Screening [Link]
• Recent Advances in Baggage Threat Detection: A Comprehensive and Systematic Survey [Link]
• Automated Detection of Threat Materials in X -Ray Baggage Inspection System (XBIS) [Link]
• Threat detection in x-ray baggage security imagery using convolutional neural networks [Link]
• X-ray baggage screening and artificial intelligence (AI) [Link]
• Material-aware Cross-channel Interaction Attention (MCIA) for occluded prohibited item detection [Link]
• A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items [Link]
• How Realistic Is Threat Image Projection for X-ray Baggage Screening? [Link]
• Cross-modal Image Synthesis within Dual-Energy X-ray Security Imagery [Link]
• Recursive CNN Model to Detect Anomaly Detection in X-Ray Security Image [Link]
• Towards More Efficient Security Inspection via Deep Learning: A Task-Driven X-ray Image Cropping Scheme [Link]
• DMA-Net: Dual multi-instance attention network for X-ray image classification [Link]
• X-ray security check image recognition based on attention mechanism [Link]
• A Data Augmentation Method for Prohibited Item X-Ray Pseudocolor Images in X-Ray Security Inspection Based on Wasserstein Generative Adversarial Network and Spatial-and-Channel Attention Block [Link]
• Enhanced threat detection in three dimensions: An image-matched comparison of computed tomography and dual-view X-ray baggage screening [Link]
• ETHSeg: An Amodel Instance Segmentation Network and a Real-world Dataset for X-Ray Waste Inspection [Link]
• Anomaly object detection in x-ray images with Gabor convolution and bigger discriminative RoI pooling [Link]
• Intelligent Detection of Dangerous Goods in Security Inspection Based on Cascade Cross Stage YOLOv3 Model [Link]
• A Lightweight Dangerous Liquid Detection Method Based on Depthwise Separable Convolution for X-Ray Security Inspection [Link]
• EAOD-Net: Effective anomaly object detection networks for X-ray images [Link]
• American National Standard for Evaluating the Image Quality of X-ray Computed Tomography (CT) Security-Screening Systems [Link]
• Automated Segmentation of Prohibited Items in X-ray Baggage Images Using Dense De-overlap Attention Snake [Link]
• Programmable Broad Learning System to Detect Concealed and Imbalanced Baggage Threats [Link]
• Few-Shot Segmentation for Prohibited Items Inspection with Patch-based Self-Supervised Learning and Prototype Reverse Validation [Link]
• Augmenting data with GANs for firearms detection in cargo x-ray images [Link]
• Weight-guided dual-direction-fusion feature pyramid network for prohibited item detection in x-ray images [Link]
• Handling occlusion in prohibited item detection from X-ray images [Link]
• Exploiting foreground and background separation for prohibited item detection in overlapping X-Ray images [Link]
• PMix: a method to improve the classification of X-ray prohibited items based on probability mixing [Link]
• Detecting prohibited objects with physical size constraint from cluttered X-ray baggage images [Link]
• Synthetic threat injection using digital twin informed augmentation [Link]
• Target Detection by Target Simulation in X-ray Testing [Link]
• Abnormal object detection in x-ray images with self-normalizing channel attention and efficient data augmentation [Link]
• Raw data processing techniques for material classification of objects in dual energy X-ray baggage inspection systems [Link]

3D

• CTIMS: Automated Defect Detection Framework Using Computed Tomography [Link]

2021

2D

• Super-resolution network for x-ray security inspection [Link]
• Information-exchange Enhanced Feature Pyramid Network (IEFPN) for Detecting Prohibited Items in X-ray Security Images [Link]
• Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security Applications [Link]
• X-ray Security Inspection Image Detection Algorithm Based on Improved YOLOv4 [Link]
• A YOLOv5s-SE model for object detection in X-ray security images [Link]
• Prohibited Items Detection in X-ray Images in YOLO Network [Link]
• Automatic and Robust Object Detection in X-Ray Baggage Inspection Using Deep Convolutional Neural Networks [Link]
• Classify and Localize Threat Items in X-Ray Imagery With Multiple Attention Mechanism and High-Resolution and High-Semantic Features [Link]
• Raw Data Processing Using Modern Hardware for Inspection of Objects in X-Ray Baggage Inspection Systems [Link]
• Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats [Link]
• Automatic Threat Detection Using Deep Neural Networks [Link]
• Towards Real-world X-ray Security Inspection: A High-Quality Benchmark And Lateral Inhibition Module For Prohibited Items Detection [Link]
• A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items [Link]
• Deep Fusion Driven Semantic Segmentation for the Automatic Recognition of Concealed Contraband Items [Link]
• Brittle Features May Help Anomaly Detection [Link]
• Deep Learning-Based X-Ray Baggage Hazardous Object Detection – An FPGA Implementation [Link]
• Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery [Link]
• Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging [Link]
• On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks [Link]
• Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark [Link]
• PANDA: Perceptually Aware Neural Detection of Anomalies [Link]
• Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition [Link]
• Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition [Link]
• Symmetric Triangle Network for Object Detection Within X-ray Baggage Security Imagery [Link]
• Anomaly Detection in X-ray Security Imaging: a Tensor-Based Learning Approach [Link]
• Automated Threat Objects Detection with Synthetic Data for Real-Time X-ray Baggage Inspection [Link]
• Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images [Link]
• An X-ray Image Enhancement Algorithm for Dangerous Goods in Airport Security Inspection [Link]
• Baggage Threat Detection Under Extreme Class Imbalance [Link]
• Detecting Overlapped Objects in X-Ray Security Imagery by a Label-Aware Mechanism [Link]

3D CT

• Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery [Link]
• On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening [Link]
• SliceNets — A Scalable Approach for Object Detection in 3D CT Scans [Link]
• DEBISim: A simulation pipeline for dual energy CT-based baggage inspection systems [Link]

2020

2D

• Learning-based Material Classification in X-ray Security Images [Link]
• Multi-label X-ray Imagery Classification via Bottom-up Attention and Meta Fusion [Link]
• Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery [Link]
• Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module [Link]
• Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion [Link]
• Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans [Link]
• Automatic Threat Detection in Baggage Security Imagery using Deep Learning Models [Link]
• Automatic Threat Detection in Single, Stereo (Two) and Multi View X-Ray Images [Link]
• Detecting Prohibited Items in X-Ray Images: a Contour Proposal Learning Approach [Link]
• Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images [Link]
• X-Ray Baggage Inspection With Computer Vision: A Survey [Link]
• Data Augmentation of X-Ray Images in Baggage Inspection Based on Generative Adversarial Networks [Link]

3D CT

• Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery [Link]
• On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery [Link]
• A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes [Link]
• An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening [Link]

2019

2D

• Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery [Link]
• On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery [Link]
• Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items [Link]
• On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery [Link]
• The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composite X-ray Imagery [Link]
• Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery [Link]
• Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection [Link]
• An evaluation of deep learning based object detection strategies for threat object detection in baggage security imagery [Link]
• Deep Convolutional Neural Network Based Object Detector for X-Ray Baggage Security Imagery [Link]
• Automated firearms detection in cargo x-ray images using RetinaNet [Link]
• Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection [Link]
• “Unexpected Item in the Bagging Area”: Anomaly Detection in X-Ray Security Images [Link]
• Limits on transfer learning from photographic image data to X-ray threat detection [Link]
• Data Augmentation for X-Ray Prohibited Item Images Using Generative Adversarial Networks [Link]
• Modified Adaptive Implicit Shape Model for Object Detection [Link]
• Graph clustering and variational image segmentation for automated firearm detection in X-ray images [Link]
• Semantic Segmentation for Prohibited Items in Baggage Inspection [Link]
• Application of Machine Learning Methods for Material Classification with Multi-energy X-Ray Transmission Images [Link]
• Handgun Detection in Single-Spectrum Multiple X-ray Views Based on 3D Object Recognition [Link]

3D CT

• On the Relevance of Denoising and Artefact Reduction in 3D Segmentation and Classification within Complex Computed Tomography Imagery [Link]

2018

2D

• On Using Deep Convolutional Neural Network Architectures for Automated Object Detection and Classification within X-ray Baggage Security Imagery [Link]
• GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training [Link]
• Multi-view X-ray R-CNN [Link]
• A GAN-Based Image Generation Method for X-Ray Security Prohibited Items [Link]
• Prohibited Item Detection in Airport X-Ray Security Images via Attention Mechanism Based CNN [Link]
• Convolutional Neural Networks for Automatic Threat Detection in Security X-Ray Images [Link]
• Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: a deep learning approach [Link]

3D CT

• Consensus relaxation on materials of interest for adaptive ATR in CT images of baggage [Link]
• Adaptive Target Recognition: A Case Study Involving Airport Baggage Screening [Link]

Earlier

2D

• An Evaluation Of Region Based Object Detection Strategies Within X-Ray Baggage Security Imagery [Link]
• On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening [Link]
• Transfer Learning Using Convolutional Neural Networks For Object Classification Within X-Ray Baggage Security Imagery [Link]
• Improving Feature-based Object Recognition for X-ray Baggage Security Screening using Primed Visual Words [Link]
• A Combinational Approach to the Fusion, De-noising and Enhancement of Dual-Energy X-Ray Luggage Images [Link]
• Improving Weapon Detection In Single Energy X-ray Images Through Pseudocoloring [Link]
• A review of X-ray explosives detection techniques for checked baggage [Link]
• A Logarithmic X-Ray Imaging Model for Baggage Inspection: Simulation and Object Detection [Link]
• Automatic Defect Recognition in X-Ray Testing Using Computer Vision [Link]
• Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection[Link]
• Inspection of Complex Objects Using Multiple-X-Ray Views [Link]
• Automated X-Ray Object Recognition Using an Efficient Search Algorithm in Multiple Views [Link]
• X-Ray Testing by Computer Vision [Link]
• Automated detection in complex objects using a tracking algorithm in multiple X-ray views [Link]
• Threat Objects Detection in X-ray Images Using an Active Vision Approach [Link]
• Object recognition in X-ray testing using an efficient search algorithm in multiple views [Link]
• Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection [Link]
• Automated Detection of Threat Objects Using Adapted Implicit Shape Model [Link]
• A review of X-ray explosives detection techniques for checked baggage [Link]
• Explosives detection systems (EDS) for aviation security [Link]

3D CT

• Geometrical Approach for the Automatic Detection of Liquid Surfaces in 3D Computed Tomography Baggage Imagery [Link]
• Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening [Link]
• Object Classification in 3D Baggage Security Computed Tomography Imagery using Visual Codebooks [Link]
• 3D Object Classification in Baggage Computed Tomography Imagery using Randomised Clustering Forests [Link]
• Investigating Existing Medical CT Segmentation Techniques within Automated Baggage and Package Inspection [Link]
• Radon Transform based Metal Artefacts Generation in 3D Threat Image Projection [Link]
• A Comparison of 3D Interest Point Descriptors with Application to Airport Baggage Object Detection in Complex CT Imagery [Link]
• A Distance Weighted Method for Metal Artefact Reduction in CT [Link]
• An Experimental Survey of Metal Artefact Reduction in Computed Tomography [Link]
• An Evaluation of CT Image Denoising Techniques Applied to Baggage Imagery Screening [Link]
• Fully Automatic 3D Threat Image Projection: Application to Densely Cluttered 3D Computed Tomography Baggage Images [Link]
• A Comparison of Classification Approaches for Threat Detection in CT based Baggage Screening [Link]
• A Novel Intensity Limiting Approach to Metal Artefact Reduction in 3D CT Baggage Imagery [Link]
• A 3D Extension to Cortex Like Mechanisms for 3D Object Class Recognition [Link]
• Object Recognition using 3D SIFT in Complex CT Volumes [Link]
• A Classifier based Approach for the Detection of Potential Threats in CT based Baggage Screening [Link]
• A review of automated image understanding within 3D baggage computed tomography security screening [Link]
• A volumetric object detection framework with dual-energy CT [Link]
• Exact Reconstruction for Dual Energy Computed Tomography Using an H-L Curve Method [Link]
• Automatic segmentation of CT scans of checked baggage [Link]
• Automatic Segmentation of Unknown Objects, with Application to Baggage Security [Link]
• ALERT Strategic Studies [Link]
• Joint metal artifact reduction and segmentation of CT images using dictionary-based image prior and continuous-relaxed potts model [Link]
• Using Threat Image Projection Data Forassessing Individual Screener Performance [Link]
• 3D threat image projection [Link]
• Learning-Based Object Identification and Segmentation Using Dual-Energy CT Images for Security [Link]