---
title: 'OPIXray: Occluded X-Ray Benchmark'
url: https://www.emergentmind.com/topics/opixray
type: topic
---

# OPIXray: Occluded X-Ray Benchmark

Searching arXiv for recent and foundational papers on OPIXray and related benchmarks/methods.
OPIXray, short for **Occluded Prohibited Items X-ray**, is a baggage X-ray object detection benchmark centered on **occluded prohibited items** in security inspection imagery. Introduced by Wei et al. as the first high-quality object detection dataset for this setting, it has been used to study cutter-like threat detection under overlap, clutter, synthetic occlusion, detector architecture choice, training strategy, lossy compression, and cross-benchmark evaluation. Across the literature, OPIXray is consistently defined by its focus on five cutter-related categories, its official train/test split, and its explicit subdivision of the test set by occlusion severity [2004.08656][2110.04906][2507.17508].

## 1. Dataset identity and composition

OPIXray contains **8,885** X-ray images and focuses on five prohibited item categories: **Folding Knife (FO)**, **Straight Knife (ST)**, **Scissor (SC)**, **Utility Knife (UT)**, and **Multi-tool Knife (MU)**. The original benchmark reports **7,109** training images and **1,776** testing images, with all images stored as **JPEG** at resolution **1225 × 954**. The category totals reported in the benchmark are **1,993** folding knives, **1,044** straight knives, **1,863** scissors, **1,978** utility knives, and **2,042** multi-tool knives [2004.08656].

Later work describes OPIXray as **RGB**, **dual-energy**, and **pseudo-colour** X-ray imagery, and emphasizes that it is a detection benchmark for **widely occurring prohibited items** in baggage screening. One comparative evaluation further characterizes the test set as containing **1,772 objects, all “medium”**, for COCO-style size analysis [2110.04906][2507.17508].

| Property | Value |
|---|---|
| Total images | 8,885 |
| Modality | RGB / pseudo-colour dual-energy X-ray |
| Resolution | 1225 × 954 JPEG |
| Classes | FO, ST, SC, UT, MU |
| Split | 7,109 train / 1,776 test |
| Annotation | Bounding boxes |
| Test stratification | OL1, OL2, OL3 |

The benchmark is often summarized as approximately **one instance per image**, and one later survey states that it has **8,885 instances** over **8,885 images**. The original benchmark is more precise: **35 images contain more than one prohibited item**, with **30** such images in training and **5** in testing [2507.17508][2004.08656]. This suggests that OPIXray is predominantly, but not absolutely, a single-instance benchmark.

## 2. Construction, annotation, and imaging assumptions

The original OPIXray paper states that the dataset uses **real luggage backgrounds scanned by airport X-ray machines**, while the prohibited items are **synthesized into those backgrounds using professional software used in airport training systems**. Annotations were performed by **professional security inspectors from an international airport**, and the annotation workflow was **double-checked** using standards described as similar to **PASCAL VOC** quality control [2004.08656].

Subsequent papers describe OPIXray as an **“Artificial Synthesis”** dataset in which prohibited objects are synthetically placed into luggage images to create occlusion, while also stressing that the scenes are meant to mimic the **randomly stacked and overlapping reality of personal baggage**. Another comparative study frames the collection context as an **international airport**, with **security inspectors asked to simulate real-world cases** in which objects are stacked in luggage to produce **varying degrees of object occlusion** [2108.09917][2507.17508].

The benchmark therefore occupies a distinctive position in the X-ray literature. It is neither a purely naturalistic stream of unmodified checkpoint imagery nor a purely abstract synthetic dataset. A plausible implication is that OPIXray was designed to preserve operational baggage appearance while giving researchers explicit control over occlusion difficulty.

## 3. Evaluation protocol and occlusion stratification

A defining feature of OPIXray is the subdivision of its **test set** into three occlusion regimes:

- **OL1**: no or slight occlusion  
- **OL2**: partial occlusion  
- **OL3**: severe or full occlusion  

The original benchmark reports the following test-set counts by occlusion level: **922** instances in OL1, **548** in OL2, and **306** in OL3. Per category, OL1 contains **206 FO**, **88 ST**, **160 SC**, **214 UT**, **255 MU**; OL2 contains **148 FO**, **84 ST**, **126 SC**, **88 UT**, **105 MU**; OL3 contains **50 FO**, **63 ST**, **83 SC**, **41 UT**, **70 MU** [2004.08656]. Later work retains the same three-way structure, sometimes writing the subsets as **OP1**, **OP2**, and **OP3** [2108.09603].

Most OPIXray papers report **mAP at IoU = 0.5**, following VOC-style detection evaluation. One operational study makes the AP definition explicit as

$$
\text{AP} = \int_0^1 p(r)\,dr,
$$

with mAP defined as the mean AP over the five OPIXray classes [2110.04906]. More recent unified evaluations also report **mAP$^{50}$** and **mAP$^{50:95}$**, and provide results separately for **OL1**, **OL2**, **OL3**, and the overall test set [2507.17508].

Because the literature mixes **VOC mAP@0.5**, **mAP$^{50}$**, **mAP$^{50:95}$**, different image sizes, and different epoch schedules, direct numerical comparison across papers requires care. This is not a contradiction in the benchmark itself; it reflects evolving evaluation practice around the same dataset.

## 4. OPIXray as a methodological testbed

OPIXray has been used to evaluate several distinct classes of methods. In the original benchmark paper, Wei et al. proposed the **De-occlusion Attention Module (DOAM)**, a plug-and-play module combining **Edge Guidance** and **Material Awareness** to refine detector features for occluded X-ray objects [2004.08656]. A later extension added an **over-sampling training strategy** and reported results under the name **DOAM-O**, explicitly targeting hard, high-occlusion samples [2103.00809].

Tao et al. used OPIXray to evaluate the **Lateral Inhibition Module (LIM)**, a feature-pyramid plug-in composed of **Bidirectional Propagation (BP)** and **Boundary Activation (BA)**. In that work, OPIXray served as the complementary benchmark to **HiXray** for testing detectors under **synthetic occlusion scenarios**, and LIM was compared directly against **DOAM** on OPIXray [2108.09917].

Operational detector studies have treated OPIXray as a benchmark for architecture and deployment trade-offs. One such study compared **Cascade R-CNN** and **FreeAnchor**, examined **JPEG compression robustness**, and systematically evaluated augmentations such as **RandomCrop**, **MixUp**, **BboxMixUp**, **CutMix**, and **ClassCutMix** on OPIXray [2110.04906].

OPIXray has also become a testbed for **DETR-like anti-overlap designs**. **AO-DETR** introduced **Category-Specific One-to-One Assignment (CSA)** and **Look Forward Densely (LFD)** on top of **DINO**, explicitly targeting foreground-background coupling and edge blurring in X-ray imagery [2403.04309]. **MMCL** added **Multi-Class Min-Margin Contrastive Learning** to deformable DETR-based detectors by regularizing decoder content queries [2406.03176]. **CSPCL** further aligned content queries with classifier-weight prototypes using **Intra-Class Truncated Attraction (ITA)** and **Inter-Class Adaptive Repulsion (IAR)** losses [2501.16665].

Beyond supervised detection, OPIXray has supported alternative formulations. One unsupervised study treated prohibited items as anomalies relative to normal baggage content and evaluated a **Gaussian-Weighted Fourier Stylization (GW-FS)** plus encoder-decoder anomaly segmentation pipeline on OPIXray without retraining the reconstruction network on OPIXray itself [2107.07333]. Another line of work used OPIXray for **tensor-pooling contour instance segmentation**, emphasizing multi-scale contours rather than anchor or query design [2108.09603].

## 5. Empirical findings on OPIXray

The original benchmark established strong early baselines. On OPIXray, **SSD** achieved **70.89** mAP, **YOLOv3** achieved **78.21**, and **FCOS** achieved **82.02**. With DOAM, these rose to **74.01**, **79.25**, and **82.41**, respectively. Across occlusion levels, SSD+DOAM reached **77.87** on OL1, **72.45** on OL2, and **70.78** on OL3, compared with **75.45**, **69.54**, and **66.30** for plain SSD; the relative gain increased with occlusion severity [2004.08656]. DOAM-O later reported **74.57** for SSD, **80.40** for YOLOv3, and **83.80** for FCOS, with particularly large gains on the **Straight Knife** category [2103.00809].

Later architectural studies pushed the reported OPIXray numbers higher. In the operational comparison of **Cascade R-CNN** and **FreeAnchor**, **FreeAnchor with a ResNet-50 backbone** achieved **87.7** mAP on OPIXray, compared with **85.9** for **Cascade R-CNN** and **82.4** for **FCOS+DOAM (Wei et al.)** in the comparison table. The same study reported that **RandomCrop** gave the largest single augmentation gain on OPIXray, improving **FreeAnchor by +3.65 mAP** and **Cascade R-CNN by +3.64**, while **MixUp** gave **+0.9** for FreeAnchor and **+0.8** for Cascade R-CNN. Under JPEG compression, FreeAnchor dropped only from **87.7** to **87.2** at **quality 50** when trained on uncompressed images, and retraining on compressed images raised performance at **quality 10** from **77.4** back to **87.4** [2110.04906].

DETR-based models produced another round of gains. Under a unified **320 × 320**, **12 epoch** setup against general detectors, **AO-DETR** reached **79.2** mAP with **ResNet-50** and **80.8** with **Swin-L**, exceeding the corresponding **DINO** baselines of **78.2** and **80.0**. In a higher-capacity OPIXray comparison against specialized prohibited-item detectors, **AO-DETR** reached **87.2** with **ResNet-50** and **89.0** with **Swin-L** at **640 × 640** and **15 epochs** [2403.04309].

Contour-driven methods also remained competitive. The tensor-pooling contour instance segmentation framework reported **0.8396** mAP on OPIXray, with occlusion-level scores of **0.7946** on OP1, **0.7382** on OP2, and **0.7291** on OP3, surpassing both **TST** and **FCOS+DOAM** in that comparison [2108.09603]. In the unsupervised anomaly-detection formulation, the reconstruction-based system reported **0.7483** mAP on OPIXray, slightly below **TST** at **0.7532** but above **DOAM** at **0.7401** and **DOAM-O** at **0.7457**; it also reported **F1 = 0.6560** against **0.3074** for **GANomaly** and **0.3464** for **Skip-GANomaly** [2107.07333].

The broadest later comparison came from a six-dataset unified evaluation. On OPIXray, **D(DINO, Swin-B)** and **D(Co-DETR, Swin-B)** achieved the highest overall **mAP$^{50}$**, both at **0.928**, while **D(YOLOv8, Next-ViT-S)** achieved the highest **mAP$^{50:95}$** at **0.429**. For the occlusion subsets, **D(YOLOv8, Next-ViT-S)** had the highest **mAP$^{50:95}$** on **OL1**, **OL2**, and **OL3**, with **0.444**, **0.424**, and **0.417**, respectively. The same study also reported that **D(YOLOv8+DOAM, CSPDarkNet53)** and **D(YOLOv8+LIM, CSPDarkNet53)** underperformed the plain **D(YOLOv8, CSPDarkNet53)** baseline on OPIXray, despite earlier gains reported for DOAM and LIM in older detector families [2507.17508].

## 6. Position in the X-ray literature

OPIXray occupies a specialized but influential niche in X-ray security research. Compared with **HiXray**, it is narrower in category diversity but more explicitly **occlusion-focused**; compared with **SIXray**, it is a true **object detection** benchmark rather than an image-level classification resource; compared with **EDS** and **PIDray**, it is not designed to probe **domain shift** or **hidden-item** behavior as directly. One 2025 evaluation therefore uses OPIXray as the benchmark “for occlusion,” alongside other datasets used for domain shift, hidden objects, or broader category diversity [2108.09917][2507.17508].

A common misconception is to treat OPIXray as a general-purpose proxy for all X-ray prohibited-item detection. The benchmark is explicitly limited to **five cutter-related classes**, and multiple papers note its relatively small scale and restricted threat diversity. One operational study states that OPIXray has a “relatively small number of samples and limited diversity of threat categories,” while later comparisons emphasize that it is **knife-dominated** and does not include the broader threat space covered by datasets such as HiXray or PIDray [2110.04906][2507.17508].

Another recurrent issue is methodological transfer. Earlier papers showed clear gains from **DOAM**, **DOAM-O**, and **LIM** when added to detectors such as **SSD**, **YOLOv3**, **FCOS**, **SSD**, **FCOS**, and **YOLOv5** [2004.08656][2103.00809][2108.09917]. The later unified evaluation, however, found that legacy X-ray-specific modules could degrade performance when transplanted into **YOLOv8**, and explicitly described this as a potentially “counter-intuitive” outcome [2507.17508]. This suggests that OPIXray is not only a benchmark for occlusion robustness but also a benchmark for **architectural compatibility**.

The benchmark’s continuing relevance lies in that role. It remains a standard site for testing whether a detector, feature pyramid, attention mechanism, query-assignment strategy, contrastive regularizer, or anomaly-localization system can handle **overlap**, **partial visibility**, and **boundary ambiguity** in pseudo-colour baggage X-ray imagery. At the same time, the literature consistently indicates that OPIXray is best interpreted as one component of a broader evaluation suite rather than as a complete stand-in for the operational X-ray screening problem.

Source: https://www.emergentmind.com/topics/opixray