| Dataset | Year | Classes | Images | Annotations | Type | Scene | Application | Availability | |||
| Total | Prohibited | Image | Bbox | Mask | |||||||
| GDXray Dbf6 Dbf3 Liu et al. SIXray OPIXray HiXray PIDray | 2015 2017 2018 2019 2019 2020 2021 2021 | 3 6 3 6 6 5 8 12 | 8150 11627 7603 32253 1059231 8885 45364 47677 | 8150 11627 7603 12683 8929 8885 45364 47677 | √ √ √ √ √ √ √ √ | √ √ √ √ √ √ √ √ | √ | Real Real Real Real Real Synthetic Real Real | - - - S S A A S + A + R | C + O C + O C + O C + O C + O C + O C + O C + O + I | √ √ √ √ √ |
【2022】
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging：PR，2022【原文】
Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module：ACM MM，2020 OPIXray 【原文】
Towards More Efficient Security Inspection via Deep Learning: A Task-Driven X-ray Image Cropping Scheme：Micromachines (Basel)，2022【原文】
EAOD-Net: Effective anomaly object detection networks for X-ray images：IET Image Processing，2022【原文】
A Lightweight Dangerous Liquid Detection Method Based on Depthwise Separable Convolution for X-Ray Security Inspection：Computational Intelligence and Neuroscience，2022【原文】
【2021】
Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery：ICMLA，2021【原文】
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items：TSMC，2021【原文】
Towards Real-world X-ray Security Inspection: A High-Quality Benchmark And Lateral Inhibition Module For Prohibited Items Detection：ICCV，2021 HiXray【原文】
Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark：ICCV，2021 【原文】【翻译】
An automated detection model of threat objects for X-ray baggage inspection based on depthwise separable convolution：J Real-Time Image Proc，2021【原文】
Benign Object Detection and Distractor Removal in 2D Baggage Scans：PRIP, TU Wien，2021【原文】
On the Impact of Using X-Ray Energy Response Imagery for Object Detection Via Convolutional Neural Networks：ICIP，2021【原文】
Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images ：Applied Sciences，2021 【原文】
Unsupervised anomaly instance segmentation for baggage threat recognition：J Ambient Intell Human Comput，2021【原文】
Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images ：arXiv:2103.00809，2021【原文】
【2020】
A novel enhanced region proposal network and modified loss function : Threat object detection in secure screening using deep learning：Journal of Supercomputing，2020【原文】
Data Augmentation of X-Ray Images in Baggage Inspection Based on Generative Adversarial Networks：IEEE Access，2020【原文】
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans： arXiv:2004.06780，2020【原文】
【2019】
YOLO-based Threat Object Detection in X-ray Images：HNICEM，2019【原文】
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery：ICMLA，2019【原文】
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery： ICMLA，2019【原文】
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery：ICMLA，2019【原文】
SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images：CVPR，2019【原文】
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection：IJCNN，2019【原文】
KNN-Based Automatic Cropping for Improved Threat Object Recognition in X-Ray Security Images：Journal of IKEEE，2019 【原文】
Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection ： arXiv:1912.06329，2019【原文】
【2018】
“Unexpected Item in the Bagging Area”: Anomaly Detection in X-Ray Security Images：TIFS，2018【原文】
Multi-view X-Ray R-CNN：GCPR，2018 【原文】
GANomaly: Semi-supervised Anomaly Detection via Adversarial Training：ACCV，2018【原文】
【2017】
An evaluation of region based object detection strategies within X-ray baggage security imagery：ICIP，2017【原文】
