Terahertz spectral features detection and accuracy identification of explosives in high humidity environment

ZENG Ziwei; JIN Shangzhong; LI Hongguang; JIANG Li; CHU Junwei

doi:10.37188/OPE.20233107.1065

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Terahertz spectral features detection and accuracy identification of explosives in high humidity environment

Information Sciences | 更新时间：2026-09-12

- Terahertz spectral features detection and accuracy identification of explosives in high humidity environment
- Optics and Precision Engineering Vol. 31, Issue 7, Pages: 1065-1073(2023)
- 作者机构：
  
  1.中国计量大学光学与电子科技学院，浙江杭州 310018
  2.西安应用光学研究所，陕西西安710065
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233107.1065
  CLC： U298;O441.4
- Received：01 August 2022，
  
  Revised：2022-09-20，
  
  Published：10 April 2023
- 稿件说明：
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曾子威,金尚忠,李宏光等.高湿度环境下爆炸物太赫兹光谱的特征提取与精准识别[J].光学精密工程,2023,31(07):1065-1073.

ZENG Ziwei,JIN Shangzhong,LI Hongguang,et al.Terahertz spectral features detection and accuracy identification of explosives in high humidity environment[J].Optics and Precision Engineering,2023,31(07):1065-1073.
曾子威,金尚忠,李宏光等.高湿度环境下爆炸物太赫兹光谱的特征提取与精准识别[J].光学精密工程,2023,31(07):1065-1073. DOI： 10.37188/OPE.20233107.1065.

ZENG Ziwei,JIN Shangzhong,LI Hongguang,et al.Terahertz spectral features detection and accuracy identification of explosives in high humidity environment[J].Optics and Precision Engineering,2023,31(07):1065-1073. DOI： 10.37188/OPE.20233107.1065.

摘要

材料太赫兹吸收谱的指纹特性已被广泛应用于物质识别，但实际大气环境下，水蒸气对太赫兹波的强烈吸收会导致光谱严重振荡，假峰、弱峰、混叠峰相继增多，严重影响寻峰比对的精度及物质识别的能力。针对上述问题，提取相对湿度为2%，15%，35%，45%和60%环境下爆炸物的太赫兹吸收光谱信息数据，利用连续小波变换将光谱在频域上展开得到具有特征唯一性的频域尺度图；再基于深度学习方法，以ResNet-50网络模型为基本网络结构，对上述5种不同湿度环境下得到的爆炸物频域尺度图进行网络分类训练，其测试集分类准确率达96.6%。为验证该技术在未经训练湿度样本下的有效性，将相对湿度为50%，55%和67%时爆炸物的时域信号送入该识别系统，分类准确率可达96.2%。实验结果表明，基于小波变换与ResNet-50网络分类训练的太赫兹物质识别方法相比于传统寻峰方法大幅提升了爆炸物在高湿度环境下的识别准确率，规避了降噪、平滑等一系列复杂预处理操作，极大拓展了太赫兹光谱探测技术的工程适应性，为山地、森林、洼地等高湿度、极复杂的作战环境下精确探测、识别地雷等爆炸物提供了技术支持。

Abstract

The fingerprint characteristics of the terahertz absorption spectrum of materials have been widely used in material identification， but the strong absorption of terahertz waves by water vapor in the actual atmospheric environment will cause the spectrum to oscillate severely； there will be increasing false， weak， and aliased peaks. These phenomena have seriously affected the accuracy of peak-finding comparison and the ability of substance identification. In spite of this， on the basis of extracting the terahertz absorption spectrum of explosives at relative humidity of 2%， 15%， 35%， 45%， and 60%， the continuous wavelet transform is expanded in the frequency domain to obtain a unique characteristic. Then， the network training is carried out on the frequency domain scale maps of explosives obtained under the above 5 different humidity conditions based on the deep learning method with the ResNet-50 network model as the basic network structure； the classification accuracy of the test can be up to 96.6%. To verify the effectiveness of the technology under untrained humidity samples， the time-domain signals of explosives at relative humidity of 50%， 55%， and 67% were fed into the identification system； the classification accuracy could reach 96.2%. Experiments show that a new terahertz material identification method， based on wavelet transform and ResNet-50 network classification training， greatly improves the accuracy of material identification in high humidity environment compared with the traditional peak-finding method. In addition， it avoids a series of complex preprocessing operations such as noise reduction and smoothing， and considerably expands the engineering adaptability of terahertz spectral detection technology. It provides help for accurate detection and identification of mines and other explosives in high humidity and extremely complex special operations environments such as mountains， forests， and depressions.

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references

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