隐匿危险品高准确度太赫兹光谱识别方法  

High-accuracy terahertz spectral identification method for concealed dangerous goods

作　　者：曾子威 李宏光[2] 郭宇烽 廖文焘 ZENG Ziwei;LI Hongguang;GUO Yufeng;LIAO Wentao(College of Optical and Electronic Technology,China Jiliang University,Hangzhou 310018,China;Xi'an Institute of Applied Optics,Xi'an 710065,China)

机构地区：[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

出　　处：《量子电子学报》2023年第3期340-348,共9页Chinese Journal of Quantum Electronics

基　　金：国防科技工业局技术基础科研计划项目(科工技[2018]294号)。

摘　　要：爆炸物等危险品的分子振动和转动能级在太赫兹频谱段具有独特的指纹谱特性,且太赫兹波对非极性物质及介电材料有较强的透过性及低能性,因此利用太赫兹光谱可以实现障碍物隐匿复杂环境下的危险品无损探测。目前各种相关材料的太赫兹吸收光谱标准库并不完善,且市面上各类太赫兹光谱仪硬件参数不同、检测标准不统一,导致单纯依赖特征吸收峰的识别方法并不可靠。针对上述问题,提出一种不依赖于吸收峰准确性的物质识别技术路线:提取物质在不同频率分辨率、不同障碍物隐匿情况下的太赫兹吸收谱,利用Marr小波变换在频域上展开得到具有特征唯一性的小波频域尺度图,建立样本集;其次,结合迁移学习方法,利用Xception网络对样本集进行训练识别。实验结果表明,此方法可以很好地对不同障碍物隐匿环境中的危险品进行分类识别,识别准确率可达94%。说明此方法的识别准确性不受系统频率分辨率即吸收谱精确度等系统因素影响,为邮件及快递包裹等障碍物隐匿危险品无损检测、定性识别提供了一种新的技术思路。The molecular vibration and rotation energy levels of explosives have unique fingerprint characteristics in the terahertz spectrum,and terahertz wave has strong permeability and low energy to non-polar substances and dielectric materials.Therefore,the use of terahertz spectrum can realize the non-destructive detection of dangerous goods in hidden environment.However,the standard library of terahertz absorption spectroscopy of materials is not perfect presently,the parameters of terahertz spectrometers on the market are different,and the detection standards are not uniform,resulting in the unreliable identification methods solely relying on absorption peaks.To address the problems,an identification technical route that no longer depends on the absorption peaks is proposed.In the method,firstly,terahertz absorption spectrum of substances with different frequency resolutions and different obstacle hidden conditions are extracted,the continuous wavelet transform of Marr is used to get a wavelet frequency domain scale map with unique characteristics,and then a data set is established.Secondly,combined with the transfer learning method,the transfer learning of Xception network is used to train and identify the data set.Experimental results show that this method is very effective in identifying explosive dangerous goods with different obstacle hidden conditions,and the recognition rates can reach 94%.It is indicated that the recognition accuracy of the proposed method can not be affected by system factors such as frequency resolution,which provides a new technical approach for non-destructive identification of dangerous goods hidden by obstacles such as package.

关 键 词：光谱学 太赫兹光谱 频率分辨率 Marr小波变换 Xception迁移学习 

分 类 号：O433.1[机械工程—光学工程]