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Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network.

Jinsong Zhang1, Wenjie Xing2, Mengdao Xing3

  • 1National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China. jszhang_1@stu.xidian.edu.cn.

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|July 20, 2018
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Summary
This summary is machine-generated.

This study introduces a deep learning system for detecting concealed items using terahertz imaging. The method enhances security screening by accurately identifying contraband with real-time detection capabilities.

Keywords:
Faster R-CNNdeep learningterahertz image detectionthreshold segmentationtransfer learning

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Area of Science:

  • Optics and Photonics
  • Computer Science
  • Security Technology

Background:

  • Terahertz (THz) imaging offers non-ionizing, clothing-penetrating capabilities, making it ideal for security applications.
  • Current security screening methods face limitations in detecting concealed items effectively and in real-time.
  • Deep learning presents a promising approach for enhancing the performance and speed of THz imaging analysis.

Purpose of the Study:

  • To develop and validate a deep learning-based system for detecting concealed weapons and contraband using terahertz imaging.
  • To improve the accuracy and real-time detection speed of security screening processes.
  • To address the challenges associated with analyzing the unique characteristics of terahertz images.

Main Methods:

  • Collection and labeling of a comprehensive dataset of terahertz images.
  • Development of a classification method leveraging transfer learning for terahertz image analysis.
  • Implementation of an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) with threshold segmentation for independent object detection.

Main Results:

  • The proposed deep learning system demonstrated high effectiveness in detecting concealed items in terahertz images.
  • Experimental results confirmed the efficiency and real-time detection capabilities of the developed method.
  • The Faster R-CNN approach, enhanced with threshold segmentation, proved successful in distinguishing between human bodies and concealed objects.

Conclusions:

  • The developed deep learning system offers a robust solution for concealed item detection in terahertz imaging.
  • The proposed method significantly enhances the performance and speed of security screening applications.
  • This research validates the potential of advanced deep learning techniques for advancing terahertz imaging security solutions.