# PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection

arXiv: https://arxiv.org/abs/2211.10763 · HTML: https://mlsanity.com/paper/2211.10763/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 67 of its 82 references that are in the map.
- First posted on arXiv 19 Nov 2022.

## Builds on (most cited first, 30 of 67)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019)

## Built on by (most cited first, 17 of 17)

- [Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision](https://mlsanity.com/paper/2402.08960/) (2024)
- [AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection](https://mlsanity.com/paper/2403.04309/) (2024)
- [Vision as Unified Multimodal Generation](https://mlsanity.com/paper/2607.06560/) (2026)
- [STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection](https://mlsanity.com/paper/2504.02823/) (2025)
- [CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors](https://mlsanity.com/paper/2501.16665/) (2025)
- [Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery](https://mlsanity.com/paper/2404.12285/) (2024)
- [gen2seg: Generative Models Enable Generalizable Instance Segmentation](https://mlsanity.com/paper/2505.15263/) (2025)
- [Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation](https://mlsanity.com/paper/2505.06280/) (2025)
- [XSeg: A Large-scale X-ray Contraband Segmentation Benchmark For Real-World Security Screening](https://mlsanity.com/paper/2604.03706/) (2026)
- [Falcon: Functional Assembly and Language for Compositional Reasoning in X-ray](https://mlsanity.com/paper/2606.25701/) (2026)
- [BGM: Background Mixup for X-ray Prohibited Items Detection](https://mlsanity.com/paper/2412.00460/) (2024)
- [X-ray illicit object detection using hybrid CNN-transformer neural network architectures](https://mlsanity.com/paper/2505.00564/) (2025)
- [Prohibited Items Segmentation via Occlusion-aware Bilayer Modeling](https://mlsanity.com/paper/2506.11661/) (2025)
- [FOAM: A General Frequency-Optimized Anti-Overlapping Framework for Overlapping Object Perception](https://mlsanity.com/paper/2506.13501/) (2025)
- [Taming Generative Synthetic Data for X-ray Prohibited Item Detection](https://mlsanity.com/paper/2511.15299/) (2025)
- [PrAda: Few-Shot Visual Adaptation for Text-Prompted Segmentation](https://mlsanity.com/paper/2605.19623/) (2026)
- [Prompting Diffusion Models for Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2606.22660/) (2026)

## Abstract (arXiv)

Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance, and occlusion. Most previous methods rarely touch the cases where the prohibited items are deliberately hidden in messy objects because of the scarcity of large-scale datasets, hindering their applications. To address this issue and facilitate related research, we present a large-scale dataset, named PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. In specific, PIDray collects 124,486 X-ray images for 12 categories of prohibited items, and each image is manually annotated with careful inspection, which makes it, to our best knowledge, to largest prohibited items detection dataset to date. Meanwhile, we propose a general divide-and-conquer pipeline to develop baseline algorithms on PIDray. Specifically, we adopt the tree-like structure to suppress the influence of the long-tailed issue in the PIDray dataset, where the first course-grained node is tasked with the binary classification to alleviate the influence of head category, while the subsequent fine-grained node is dedicated to the specific tasks of the tail categories. Based on this simple yet effective scheme, we offer strong task-specific baselines across object detection, instance segmentation, and multi-label classification tasks and verify the generalization ability on common datasets (e.g., COCO and PASCAL VOC). Extensive experiments on PIDray demonstrate that the proposed method performs favorably against current state-of-the-art methods, especially for deliberately hidden items. Our benchmark and codes will be released at https://github.com/lutao2021/PIDray.