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X-ray Imaging

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...

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PDSXray: A Benchmark Dataset for Pseudocoloring-Driven Domain Adaptation in Security X-ray Inspection.

Xiaohao Zhang¹, Jiansen Qiao², Xianyu Wang²

¹College of Information and Electrical Engineering, China Agricultural University, Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China.

Scientific Data

|April 4, 2026

Summary

This summary is machine-generated.

This study introduces a new benchmark dataset for security X ray imaging, addressing pseudocoloring driven domain shift (PDS) and hardware variations. It enables better evaluation of object detection models in diverse X ray security applications.

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Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Security X ray imaging datasets primarily address hardware-induced domain shifts.
Pseudocoloring driven domain shift (PDS) from varying pseudocoloring schemes is understudied.

Purpose of the Study:

To introduce a benchmark dataset for security X ray inspection domain shift analysis.
To specifically address PDS and endogenous domain shifts.
To facilitate research on model generalization and robustness.

Main Methods:

Constructed a novel benchmark dataset with two subsets: synthetic (isolating PDS) and aggregated (integrating public datasets).
Applied controlled hue transformations for synthetic PDS evaluation.
Standardized annotations for reproducibility and comparability.

Main Results:

The dataset enables independent evaluation of PDS.
It supports verification of cross-domain and cross-device model generalization.
Provides a benchmark for object detection algorithms under domain shifts.

Conclusions:

The dataset addresses a critical gap in security X ray imaging research.
It offers a valuable resource for developing more robust object detection models.
Facilitates advancements in diverse X ray security inspection scenarios.