# SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images

arXiv: https://arxiv.org/abs/1901.00303 · HTML: https://mlsanity.com/paper/1901.00303/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2019.

## Built on by (most cited first, 30 of 52)

- [X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection](https://mlsanity.com/paper/2302.09491/) (2023)
- [Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning](https://mlsanity.com/paper/2212.03954/) (2022)
- [Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark](https://mlsanity.com/paper/2108.07020/) (2021)
- [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)
- [Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment](https://mlsanity.com/paper/2202.02838/) (2022)
- [Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark](https://mlsanity.com/paper/2207.11169/) (2022)
- [PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection](https://mlsanity.com/paper/2211.10763/) (2022)
- [Self-supervised visual learning in the low-data regime: a comparative evaluation](https://mlsanity.com/paper/2404.17202/) (2024)
- [AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection](https://mlsanity.com/paper/2403.04309/) (2024)
- [Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition](https://mlsanity.com/paper/2107.07333/) (2021)
- [Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition](https://mlsanity.com/paper/2108.09603/) (2021)
- [Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey](https://mlsanity.com/paper/2211.05565/) (2022)
- [Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanations](https://mlsanity.com/paper/2310.08537/) (2023)
- [Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery](https://mlsanity.com/paper/2110.04906/) (2021)
- [Cross-domain Multi-modal Few-shot Object Detection via Rich Text](https://mlsanity.com/paper/2403.16188/) (2024)
- [STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection](https://mlsanity.com/paper/2504.02823/) (2025)
- [Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images](https://mlsanity.com/paper/2103.00809/) (2021)
- [On the impact of using X-ray energy response imagery for object detection via Convolutional Neural Networks](https://mlsanity.com/paper/2108.12505/) (2021)
- [A Meta-Learning Approach to Predicting Performance and Data Requirements](https://mlsanity.com/paper/2303.01598/) (2023)
- [Illicit item detection in X-ray images for security applications](https://mlsanity.com/paper/2305.01936/) (2023)
- [Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis](https://mlsanity.com/paper/2407.15763/) (2024)
- [Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?](https://mlsanity.com/paper/2411.18082/) (2024)
- [A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items](https://mlsanity.com/paper/2201.02560/) (2022)
- [Visual inspection for illicit items in X-ray images using Deep Learning](https://mlsanity.com/paper/2310.03658/) (2023)
- [Illicit object detection in X-ray images using Vision Transformers](https://mlsanity.com/paper/2403.19043/) (2024)
- [CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors](https://mlsanity.com/paper/2501.16665/) (2025)
- [Superpowering Open-Vocabulary Object Detectors for X-ray Vision](https://mlsanity.com/paper/2503.17071/) (2025)
- [Improved YOLOv8 Detection Algorithm in Security Inspection Image](https://mlsanity.com/paper/2308.06452/) (2023)
- [Neural Network Methods for Radiation Detectors and Imaging](https://mlsanity.com/paper/2311.05726/) (2023)
- [Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery](https://mlsanity.com/paper/2404.12285/) (2024)

## Abstract (arXiv)

In this paper, we present a large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images. Our dataset, named SIXray, consists of 1,059,231 X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated. It raises a brand new challenge of overlapping image data, meanwhile shares the same properties with existing datasets, including complex yet meaningless contexts and class imbalance. We propose an approach named class-balanced hierarchical refinement (CHR) to deal with these difficulties. CHR assumes that each input image is sampled from a mixture distribution, and that deep networks require an iterative process to infer image contents accurately. To accelerate, we insert reversed connections to different network backbones, delivering high-level visual cues to assist mid-level features. In addition, a class-balanced loss function is designed to maximally alleviate the noise introduced by easy negative samples. We evaluate CHR on SIXray with different ratios of positive/negative samples. Compared to the baselines, CHR enjoys a better ability of discriminating objects especially using mid-level features, which offers the possibility of using a weakly-supervised approach towards accurate object localization. In particular, the advantage of CHR is more significant in the scenarios with fewer positive training samples, which demonstrates its potential application in real-world security inspection.