# Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection

arXiv: https://arxiv.org/abs/2108.09917 · HTML: https://mlsanity.com/paper/2108.09917/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [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)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [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)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Forward and Backward Information Retention for Accurate Binary Neural Networks](https://mlsanity.com/paper/1909.10788/) (2019)
- [Pointwise Convolutional Neural Networks](https://mlsanity.com/paper/1712.05245/) (2017)
- [Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World](https://mlsanity.com/paper/2103.01050/) (2021)
- [Focusing Attention: Towards Accurate Text Recognition in Natural Images](https://mlsanity.com/paper/1709.02054/) (2017)
- [Detecting Oriented Text in Natural Images by Linking Segments](https://mlsanity.com/paper/1703.06520/) (2017)
- [SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images](https://mlsanity.com/paper/1901.00303/) (2019)
- [Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module](https://mlsanity.com/paper/2004.08656/) (2020)
- [BiPointNet: Binary Neural Network for Point Clouds](https://mlsanity.com/paper/2010.05501/) (2020)
- [Bias-based Universal Adversarial Patch Attack for Automatic Check-out](https://mlsanity.com/paper/2005.09257/) (2020)
- [Multi-Pretext Attention Network for Few-shot Learning with Self-supervision](https://mlsanity.com/paper/2103.05985/) (2021)
- [HCIC: Hardware-assisted Control-flow Integrity Checking](https://mlsanity.com/paper/1801.07397/) (2018)

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

- [Physical Adversarial Attack meets Computer Vision: A Decade Survey](https://mlsanity.com/paper/2209.15179/) (2022)
- [X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection](https://mlsanity.com/paper/2302.09491/) (2023)
- [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)
- [Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey](https://mlsanity.com/paper/2211.05565/) (2022)
- [STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection](https://mlsanity.com/paper/2504.02823/) (2025)
- [Illicit item detection in X-ray images for security applications](https://mlsanity.com/paper/2305.01936/) (2023)
- [Modularizing while Training: A New Paradigm for Modularizing DNN Models](https://mlsanity.com/paper/2306.09376/) (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)
- [MMCL: Correcting Content Query Distributions for Improved Anti-Overlapping X-Ray Object Detection](https://mlsanity.com/paper/2406.03176/) (2024)
- [ODverse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v11](https://mlsanity.com/paper/2502.14314/) (2025)
- [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)
- [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)
- [Behavior Backdoor for Deep Learning Models](https://mlsanity.com/paper/2412.01369/) (2024)
- [BCR-Net: Boundary-Category Refinement Network for Weakly Semi-Supervised X-Ray Prohibited Item Detection with Points](https://mlsanity.com/paper/2412.18918/) (2024)
- [X-ray illicit object detection using hybrid CNN-transformer neural network architectures](https://mlsanity.com/paper/2505.00564/) (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)
- [GSA-YOLO: A High-Efficiency Framework via Structured Sparsity and Adaptive Knowledge Distillation for Real-Time X-ray Security Inspection](https://mlsanity.com/paper/2605.20669/) (2026)
- [Battery detection of XRay images using transfer learning](https://mlsanity.com/paper/2606.11779/) (2026)
- [OneFocus: Enabling Real-World X-ray Security Screening with a Unified Vision-Language Model](https://mlsanity.com/paper/2606.15663/) (2026)
- [DERA: Detached Edge-Residual Adaptation for Prohibited item Detection](https://mlsanity.com/paper/2609.12411/) (2026)

## Abstract (arXiv)

Prohibited items detection in X-ray images often plays an important role in protecting public safety, which often deals with color-monotonous and luster-insufficient objects, resulting in unsatisfactory performance. Till now, there have been rare studies touching this topic due to the lack of specialized high-quality datasets. In this work, we first present a High-quality X-ray (HiXray) security inspection image dataset, which contains 102,928 common prohibited items of 8 categories. It is the largest dataset of high quality for prohibited items detection, gathered from the real-world airport security inspection and annotated by professional security inspectors. Besides, for accurate prohibited item detection, we further propose the Lateral Inhibition Module (LIM) inspired by the fact that humans recognize these items by ignoring irrelevant information and focusing on identifiable characteristics, especially when objects are overlapped with each other. Specifically, LIM, the elaborately designed flexible additional module, suppresses the noisy information flowing maximumly by the Bidirectional Propagation (BP) module and activates the most identifiable charismatic, boundary, from four directions by Boundary Activation (BA) module. We evaluate our method extensively on HiXray and OPIXray and the results demonstrate that it outperforms SOTA detection methods.