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Prohibited Item Detection in Airport X-Ray Security Images via Attention Mechanism Based CNN

  • Maoshu Xu
  • Haigang Zhang
  • Jinfeng YangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)

Abstract

Automation of security inspections is crucial for improving the efficiency and reducing security risks. In this paper, we focus on automatically recognizing and localizing prohibited items in airport X-ray security images. A top-down attention mechanism is applied to enhance a CNN classifier to additionally locate the prohibited items. We introduce a high-level semantic feedback loop to map the targets semantic signal to the input X-ray image space for generating task-specic attention maps. And the attention maps indicate the location and general outline of prohibited items in the input images. Furthermore, to obtain more accurate location information, we combine the lateral inhibition and contrastive attention to suppress noise and non-target interference in attention maps. The experiments on the GDX-ray image dataset have demonstrated the efficiency and stability of the proposed scheme in both single target detection and multi-target detection.

Keywords

Prohibited item Detection Attention CNN 

Notes

Acknowledgments

This work was supported by the National Science Foundation of China Nos. 61379102, 61806208.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Tianjin Key Lab for Advanced Signal ProcessingCivil Aviation University of ChinaTianjinChina

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