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A Study on Detection of Prohibited Items Based on X-Ray Images with Lightweight Model.

Tianfen Liang1,2, Hao Wen2, Binyu Huang1

  • 1Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R. China, Guangzhou 510700, China.

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|September 13, 2025
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Summary

This study introduces a lightweight deep learning algorithm for automatic prohibited item detection in X-ray security screening. The new method enhances accuracy and reduces fatigue-related errors, improving public safety.

Keywords:
X-ray imagesdeep learningdepthwise separable convolutiondilated convolution spatial pyramid modulelightweightingprohibited items

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Security Technology

Background:

  • X-ray security screening is vital for public safety, but manual inspection is prone to errors due to fatigue and complex scenarios.
  • Challenges include overlapping items, variable positions, and the need for portable detection solutions.

Purpose of the Study:

  • To develop a lightweight automatic detection method for prohibited items in X-ray images.
  • To improve detection accuracy and efficiency compared to traditional manual inspection and existing automated systems.

Main Methods:

  • A deep learning algorithm utilizing a novel backbone network with residual structure and attention mechanism.
  • Integration of a dilated convolutional spatial pyramid module and depthwise separable convolution for multi-scale feature fusion.

Main Results:

  • The proposed lightweight method achieved a highest detection rate of 95.59%.
  • Demonstrated a 1.86% mean Average Precision (mAP) improvement over the YOLOv4-tiny baseline.
  • Operates at 122 Frames Per Second (FPS), indicating high efficiency.

Conclusions:

  • The developed lightweight automatic detection method effectively enhances prohibited item detection accuracy in X-ray security screening.
  • This technology offers a promising solution for improving public safety by reducing human error and increasing detection efficiency.