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<p>In the field of security X ray imaging, existing datasets mainly focus on endogenous domain shifts caused by hardware differences but do not address pseudocoloring driven domain shift (PDS), a type of domain shift resulting from different pseudocoloring schemes across devices. To fill this gap, we construct and release a benchmark dataset designed for studying domain shifts in security X ray inspection, focusing on both PDS and endogenous differences. The dataset has two complementary subsets: a synthetic subset, which applies controlled hue transformations on an endogenous domain shift dataset to isolate and evaluate PDS independently; and an aggregated subset, which integrates multiple public security datasets into a unified format for verifying cross domain and cross device model generalization. All annotations follow standardized structures with detailed descriptions to ensure reproducibility and comparability. This dataset provides a benchmark for evaluating object detection algorithms under domain shifts and supports studies on model robustness and generalization in diverse X ray security scenarios.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月4日

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  • Xiaohao Zhang,
  • Jiansen Qiao,
  • Xianyu Wang,
  • Wenjie Zhang,
  • Jianxun Li,
  • Yinxue Shi

摘要

In the field of security X ray imaging, existing datasets mainly focus on endogenous domain shifts caused by hardware differences but do not address pseudocoloring driven domain shift (PDS), a type of domain shift resulting from different pseudocoloring schemes across devices. To fill this gap, we construct and release a benchmark dataset designed for studying domain shifts in security X ray inspection, focusing on both PDS and endogenous differences. The dataset has two complementary subsets: a synthetic subset, which applies controlled hue transformations on an endogenous domain shift dataset to isolate and evaluate PDS independently; and an aggregated subset, which integrates multiple public security datasets into a unified format for verifying cross domain and cross device model generalization. All annotations follow standardized structures with detailed descriptions to ensure reproducibility and comparability. This dataset provides a benchmark for evaluating object detection algorithms under domain shifts and supports studies on model robustness and generalization in diverse X ray security scenarios.