近红外光谱成像系统在液体安检中的应用

黄立贤; 李大鹏; 吴凡; 沈志学; 曹宁翔; 段佳著; 王海峰

doi:10.11884/HPLPB201830.170180

近红外光谱成像系统在液体安检中的应用

doi: 10.11884/HPLPB201830.170180

中国工程物理研究院流体物理研究所, 四川绵阳 621900

基金项目:

国家自然科学基金项目 61575179

详细信息

作者简介:
黄立贤(1985－), 男，硕士，助理研究员，从事多光谱成像技术研究；huanglixian175@163.com

中图分类号: O433;TP731
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出版历程
- 收稿日期: 2017-05-18
- 修回日期: 2017-09-12
- 刊出日期: 2018-01-15

Near infrared multi-spectral imaging system for flammable liquid detection

Institute of Fluid Physics, CAEP, P. O. Box 919-988, Mianyang 621900, China

摘要

摘要: 易燃易爆液体的实时、远程和动态探测对于保障公共安全具有重要意义，在公安、民航和海关等领域应用前景广阔。研制了基于液晶可调滤光片的近红外光谱成像系统，通过波长优选算法大幅降低了光谱通道数，有效提高了扫描成像速度和数据处理速度，可实现对低速运动的液体危险品的远程和实时检测。利用该系统开展了不同环境下对易燃易爆液体的检测实验，结果表明该系统对静态目标检测精度为100%，对速度小于0.2 m/s的运动目标检测精度高于95%。
- 多光谱成像 /
- 液体危险品 /
- 安全检测 /
- 液晶可调滤光片 /
- 远程动态监测
Abstract: The flammable liquid detection, especially the emote flammable liquid dynamic monitoring, is of great importance to public security, customs and civil aviation. Hence, a near infrared (NIR) multi-spectral imaging system based on liquid crystal tunable filter was developed. To increase the speed of scanning imaging and data processing, the number of spectral channel was reduced remarkably by band selection method. The experiment of flammable liquid detection by the system was carried out in different environments, and the result show that the detection accuracy of the system is 100% when the object was motionless, and it is more than 95% when the movement speed of object was less than 0.2 m/s.
- multi-spectral imaging /
- flammable liquid /
- safety detection /
- liquid crystal tunable filter /
- remote dynamic monitoring

HTML全文

图 1 近红外LCTF

Figure 1. NIR liquid crystal tunable filterl (LCTF)

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图 2 近红外光谱成像系统

Figure 2. NIR imaging spectrometer

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图 3 四种液体样品的反射率曲线

Figure 3. Reflectance curve of four liquid samples

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图 4 识别准确率与波段数的关系

Figure 4. Relationship between recognition accuracy and band number

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图 5 软件流程图

Figure 5. Software flowchart

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图 6 四种液体样品静态识别结果

Figure 6. Static identification results of four liquid samples

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图 7 室外测试结果软件截图(左为橙汁、右为汽油)

Figure 7. Screenshot of software (orange juice on the left and gasoline on the right)

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表 1 含氢基团在近红外波段吸收谱带的中心位置

Table 1. Central position of hydric groups in near infrared (NIR) absoption spectrum

hydric group	central position/nm
hydric group	scaling vibration base frequency	bending vibration base frequency	combined frequency	double frequency	triple frequency	quadruplicated frequency
C－H	3400	7000	2400	1750	1300	930
N－H	3000	6250	2300	1650	1100	780
O－H	2840	7800	1900	1500	980	730

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表 2 近红外光谱成像系统指标

Table 2. Specifications of NIR imaging spectrometer

spectral range /nm	spectral resolution /nm	number of channels	volume /cm³	weight /kg	detection range /m
900~1700	20	1~40	40×18×18	3	1~10

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表 3 液体样品识别准确率统计结果

Table 3. Statistical results of accuracy of liquid sample recognition

sample movement speed/(m·s^-1)	times of unidentification when carrying flammable liquid (in 50 times)	times of false alarm with water and beverage (in 50 times)	accuracy /%
0.1	2	0	98
0.2	4	1	95
0.3	14	4	82

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参考文献(14)

[1]	胡海峰, 邱丽君, 陈志强, 等. 液体安全检查技术综述[J]. 中国体视学与图像分析, 2009, 14(3): 321-325. https://www.cnki.com.cn/Article/CJFDTOTAL-ZTSX200903021.htm Hu Haifeng, Qiu Lijun, Chen Zhiqiang, et al. Review of liquid security inspection technology. Chinese Journal of Stereology and Image Analysis, 2009, 14(3): 321-325 https://www.cnki.com.cn/Article/CJFDTOTAL-ZTSX200903021.htm
[2]	武娟, 杜振辉, 刘瑾, 等. 短波近红外光谱在现场安检中的应用研究[J]. 光谱学与光谱分析, 2008, 12(8): 30-32. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN200809038.htm Wu Juan, Du Zhenhui, Liu Jin, et al. Research on shortwave NIR spectroscopy and its application to in situ flammable liquid detection. Spectroscopy and Spectral Analysis, 2008, 12(8): 30-32 https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN200809038.htm
[3]	童庆禧, 张兵, 张立福. 中国高光谱遥感的前沿进展[J]. 遥感学报, 2016, 20(5): 689-707. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201605003.htm Tong Qingxi, Zhang Bing, Zhang Lifu. Current progress of hyperspectral remote sensing in China. Journal of Remote Sensing, 2016, 20(5): 689-707 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201605003.htm
[4]	Gowen A A, O'Donnelle P, Cullen R J. Recent applications of chemical imaging to pharmaceutical process monitoring and quality control[J]. European Journal of Pharmaceutics and Biopharmaceutics, 2008, 69: 10-22. doi: 10.1016/j.ejpb.2007.10.013
[5]	Tsuta M, Sugiyama J, Sagara Y. Near-infrared imaging spectroscopy based on sugar absorption band of melons[J]. Journal of Agricultural and Food Chemistry, 2002, 50(2): 48-52.
[6]	黄越, 王冬, 段佳, 等. 近红外光谱成像分析技术的应用进展[J]. 现代仪器, 2011, 17(5): 13-18. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYI201105003.htm Huang Yue, Wang Dong, Duan Jia, et al. Applications of near-infrared spectral imaging analyses: a review. Modern Instruments, 2011, 17(5): 13-18 https://www.cnki.com.cn/Article/CJFDTOTAL-XDYI201105003.htm
[7]	张保华, 李江波, 樊书祥, 等. 高光谱成像技术在果蔬品质与安全无损检测中的原理及应用[J]. 光谱学与光谱分析, 2014, 34(10): 2743-2751. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201410033.htm Zhang Baohua, Li Jiangbo, Fan Shuxiang, et al. Principles and applications of hyperspectral imaging technique in quality and safety inspection of fruits and vegetables. Spectroscopy and Spectral Analysis, 2014, 34(10): 2743-2751 https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201410033.htm
[8]	许明明, 张良培, 杜博, 等. 基于类别可分性的高光谱图像波段选择[J]. 计算机科学, 2015, 42(4): 274-275. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201504056.htm Xu Mingming, Zhang Liangpei, Du Bo, et al. Supervised band selection based on class separability for hyperspectral image. Computer Science, 2015, 42(4): 274-275 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201504056.htm
[9]	Ifarraguerri A. Visual method for spectral band selection[J]. IEEE Geoscience and Remote Sensing Letters, 2004, 1(2): 101-106.
[10]	Backer S D, Kempeneers P, Debruyn W, et al. A band selection technique for spectral classification[J]. IEEE Geoscience and Remote Sensing Letters, 2005, 2(3): 319-323.
[11]	闻兵工, 冯伍法, 刘伟. 基于光谱曲线整体相似性测度的匹配分类[J]. 测绘科学技术学报, 2009, 26(2): 128-131. https://www.cnki.com.cn/Article/CJFDTOTAL-JFJC200902015.htm Wen Binggong, Feng Wufa, Liu Wei. Matching and classification based on the whole comparability measure of spectral curve. Journal of Geomatics Science and Technology, 2009, 26(2): 128-131 https://www.cnki.com.cn/Article/CJFDTOTAL-JFJC200902015.htm
[12]	沈志学, 李剑峰, 张大勇, 等. 可见光液晶光谱成像系统研究[J]. 光谱学与光谱分析, 2011, 31(10): 2672-2675. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201110019.htm Shen Zhixue, Li Jianfeng, Zhang Dayong, et al. Research on LC-based spectral imaging system for visible band. Spectroscopy and Spectral Analysis, 2011, 31(10): 2672-2675 https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201110019.htm
[13]	沈志学, 李剑峰, 张大勇, 等. 液晶光谱成像技术研究进展[J]. 光学与光电技术, 2011, 9(1): 1274-1278. https://www.cnki.com.cn/Article/CJFDTOTAL-GXGD201101028.htm Shen Zhixue, Li Jianfeng, Zhang Dayong, et al. Develop of LC-based spectral imaging technology. Optics & Optoelectronic Technology, 2011, 9(1): 1274-1278 https://www.cnki.com.cn/Article/CJFDTOTAL-GXGD201101028.htm
[14]	Shen Zhixue, Li Jianfeng, Huang Lixian. Research on imaging spectrometer using LC-based tunable filter[C]//Imaging Spectrometry XVⅡ Conference, 2012.