# Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module

arXiv: https://arxiv.org/abs/2004.08656 · HTML: https://mlsanity.com/paper/2004.08656/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2020.

## Built on by (most cited first, 30 of 39)

- [Towards Accurate Post-Training Quantization for Vision Transformer](https://mlsanity.com/paper/2303.14341/) (2023)
- [Diversifying Sample Generation for Accurate Data-Free Quantization](https://mlsanity.com/paper/2103.01049/) (2021)
- [X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection](https://mlsanity.com/paper/2302.09491/) (2023)
- [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)
- [PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection](https://mlsanity.com/paper/2211.10763/) (2022)
- [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)
- [Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery](https://mlsanity.com/paper/2110.04906/) (2021)
- [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)
- [Illicit item detection in X-ray images for security applications](https://mlsanity.com/paper/2305.01936/) (2023)
- [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)
- [Visual inspection for illicit items in X-ray images using Deep Learning](https://mlsanity.com/paper/2310.03658/) (2023)
- [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)
- [Context in object detection: a systematic literature review](https://mlsanity.com/paper/2503.23249/) (2025)
- [Multi-Pretext Attention Network for Few-shot Learning with Self-supervision](https://mlsanity.com/paper/2103.05985/) (2021)
- [Improved YOLOv8 Detection Algorithm in Security Inspection Image](https://mlsanity.com/paper/2308.06452/) (2023)
- [Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery](https://mlsanity.com/paper/2404.12285/) (2024)
- [MMCL: Correcting Content Query Distributions for Improved Anti-Overlapping X-Ray Object Detection](https://mlsanity.com/paper/2406.03176/) (2024)
- [Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats](https://mlsanity.com/paper/2111.02651/) (2021)
- [Open-Vocabulary X-ray Prohibited Item Detection via Fine-tuning CLIP](https://mlsanity.com/paper/2406.10961/) (2024)
- [PAD-F: Prior-Aware Debiasing Framework for Long-Tailed X-ray Prohibited Item Detection](https://mlsanity.com/paper/2411.18078/) (2024)
- [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)
- [XSeg: A Large-scale X-ray Contraband Segmentation Benchmark For Real-World Security Screening](https://mlsanity.com/paper/2604.03706/) (2026)

## Abstract (arXiv)

Security inspection often deals with a piece of baggage or suitcase where objects are heavily overlapped with each other, resulting in an unsatisfactory performance for prohibited items detection in X-ray images. In the literature, there have been rare studies and datasets touching this important topic. In this work, we contribute the first high-quality object detection dataset for security inspection, named Occluded Prohibited Items X-ray (OPIXray) image benchmark. OPIXray focused on the widely-occurred prohibited item "cutter", annotated manually by professional inspectors from the international airport. The test set is further divided into three occlusion levels to better understand the performance of detectors. Furthermore, to deal with the occlusion in X-ray images detection, we propose the De-occlusion Attention Module (DOAM), a plug-and-play module that can be easily inserted into and thus promote most popular detectors. Despite the heavy occlusion in X-ray imaging, shape appearance of objects can be preserved well, and meanwhile different materials visually appear with different colors and textures. Motivated by these observations, our DOAM simultaneously leverages the different appearance information of the prohibited item to generate the attention map, which helps refine feature maps for the general detectors. We comprehensively evaluate our module on the OPIXray dataset, and demonstrate that our module can consistently improve the performance of the state-of-the-art detection methods such as SSD, FCOS, etc, and significantly outperforms several widely-used attention mechanisms. In particular, the advantages of DOAM are more significant in the scenarios with higher levels of occlusion, which demonstrates its potential application in real-world inspections. The OPIXray benchmark and our model are released at https://github.com/OPIXray-author/OPIXray.