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X-ray Imaging01:24

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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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A Data Augmentation Method for Prohibited Item X-Ray Pseudocolor Images in X-Ray Security Inspection Based on

Dongming Liu1,2, Jianchang Liu1,2, Peixin Yuan3

  • 1College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

Computational Intelligence and Neuroscience
|March 28, 2022
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Summary

This study introduces a novel data augmentation technique to enhance deep learning models for detecting prohibited items in X-ray security inspections. The method effectively increases the available dataset, improving detection accuracy for crucial security applications.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Deep learning for X-ray security inspection faces challenges due to limited datasets of prohibited items.
  • Scarcity of pseudocolor X-ray datasets hinders the development of effective detection models.

Purpose of the Study:

  • To propose a data augmentation method for prohibited item X-ray pseudocolor images.
  • To address the challenge of limited datasets in X-ray security inspection for prohibited item detection.

Main Methods:

  • A framework for dataset augmentation using generative adversarial networks (GANs) with a gradient penalty.
  • Development of a spatial-and-channel attention block and a new base block for the X-ray Wasserstein GAN.
  • Generation of high-quality dual-energy X-ray data and a composite strategy to create realistic pseudocolor images.

Main Results:

  • The proposed method effectively augments datasets of prohibited item X-ray pseudocolor images.
  • Object detection models utilizing the augmented data showed improved performance.
  • The generated dual-energy X-ray data and composite strategy simulated realistic item overlaps.

Conclusions:

  • The developed data augmentation method is effective for improving prohibited item detection in X-ray security inspections.
  • This approach offers a viable solution to the dataset scarcity problem in this critical security domain.