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Area of Science:

  • Computer Science
  • Security Technology

Background:

  • Object detection in X-ray security inspection shows promise for safety.
  • Research on dangerous liquid detection using X-rays is limited, focusing mainly on common items.

Purpose of the Study:

  • To propose a lightweight deep learning method for dangerous liquid detection in X-ray security inspection.
  • To improve detection accuracy and efficiency for dangerous liquids.

Main Methods:

  • Developed a dataset of seven common dangerous liquids in various postures and environments.
  • Proposed a novel detection framework using dual-energy X-ray data.
  • Designed lightweight networks using Depthwise Separable convolution and Squeeze-and-Excitation blocks for location and classification.
  • Implemented a semiautomatic labeling method for efficient data annotation.

Main Results:

  • The proposed method demonstrates superior performance compared to existing techniques.
  • Achieved improved detection accuracy by utilizing dual-energy X-ray data.
  • Reduced computational load and parameter count through lightweight network design.

Conclusions:

  • The developed lightweight method offers better performance and broader applicability for dangerous liquid detection in X-ray security.
  • The novel framework using dual-energy X-ray data enhances detection accuracy and operational efficiency.