# BCR-Net: Boundary-Category Refinement Network for Weakly Semi-Supervised X-Ray Prohibited Item Detection with Points

arXiv: https://arxiv.org/abs/2412.18918 · HTML: https://mlsanity.com/paper/2412.18918/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 32 references that are in the map.
- First posted on arXiv 25 Dec 2024.

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

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Weakly Supervised Deep Detection Networks](https://mlsanity.com/paper/1511.02853/) (2015)
- [Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework](https://mlsanity.com/paper/2103.11402/) (2021)
- [PCL: Proposal Cluster Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1807.03342/) (2018)
- [Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2303.12369/) (2023)
- [Points as Queries: Weakly Semi-supervised Object Detection by Points](https://mlsanity.com/paper/2104.07434/) (2021)
- [X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection](https://mlsanity.com/paper/2302.09491/) (2023)
- [SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images](https://mlsanity.com/paper/1901.00303/) (2019)
- [WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection](https://mlsanity.com/paper/1909.04972/) (2019)
- [Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module](https://mlsanity.com/paper/2004.08656/) (2020)
- [Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark](https://mlsanity.com/paper/2108.07020/) (2021)
- [Point-to-Box Network for Accurate Object Detection via Single Point Supervision](https://mlsanity.com/paper/2207.06827/) (2022)
- [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)
- [Semi-DETR: Semi-Supervised Object Detection with Detection Transformers](https://mlsanity.com/paper/2307.08095/) (2023)
- [PointOBB: Learning Oriented Object Detection via Single Point Supervision](https://mlsanity.com/paper/2311.14757/) (2023)
- [Group R-CNN for Weakly Semi-supervised Object Detection with Points](https://mlsanity.com/paper/2205.05920/) (2022)
- [SOOD: Towards Semi-Supervised Oriented Object Detection](https://mlsanity.com/paper/2304.04515/) (2023)
- [Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency](https://mlsanity.com/paper/2303.08686/) (2023)
- [Object Discovery via Contrastive Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/2208.07576/) (2022)
- [Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection](https://mlsanity.com/paper/2303.14960/) (2023)
- [MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection](https://mlsanity.com/paper/2303.09061/) (2023)
- [Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection](https://mlsanity.com/paper/2404.01819/) (2024)
- [I2OL-Net: Intra-Inter Objectness Learning Network for Point-Supervised X-Ray Prohibited Item Detection](https://mlsanity.com/paper/2412.03811/) (2024)

## Abstract (arXiv)

Automatic prohibited item detection in X-ray images is crucial for public safety. However, most existing detection methods either rely on expensive box annotations to achieve high performance or use weak annotations but suffer from limited accuracy. To balance annotation cost and detection performance, we study Weakly Semi-Supervised X-ray Prohibited Item Detection with Points (WSSPID-P) and propose a novel Boundary-Category Refinement Network (BCR-Net) that requires only a few box annotations and a large number of point annotations. BCR-Net is built based on Group R-CNN and introduces a new Boundary Refinement (BR) module and a new Category Refinement (CR) module. The BR module develops a dual attention mechanism to focus on both the boundaries and salient features of prohibited items. Meanwhile, the CR module incorporates contrastive branches into the heads of RPN and ROI by introducing a scale- and rotation-aware contrastive loss, enhancing intra-class consistency and inter-class separability in the feature space. Based on the above designs, BCR-Net effectively addresses the closely related problems of imprecise localization and inaccurate classification. Experimental results on public X-ray datasets show the effectiveness of BCR-Net, achieving significant performance improvements to state-of-the-art methods under limited annotations.