[논문]Dynamic one-shot target detection and classification using a pseudo-Siamese network and its application to Raman spectroscopy

Park, Jae-Hyeon; Yu, Hyeong-Geun; Park, Dong-Jo; Nam, Hyunwoo; Chang, Dong Eui

doi:10.1039/d1an01352a

[해외논문] Dynamic one-shot target detection and classification using a pseudo-Siamese network and its application to Raman spectroscopy

The Analyst : An International Journal of Analytical and Bioanalytical Science, v.146 no.22, 2021년, pp.6997 - 7004

Park, Jae-Hyeon (School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST)) , Yu, Hyeong-Geun (School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST)) , Park, Dong-Jo (School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST)) , Nam, Hyunwoo (The CRB Defense Technology Directorate, Agency for Defense Development) , Chang, Dong Eui (School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST))

Abstract ▼ AI-Helper

Target detection and classification by Raman spectroscopy are important techniques for biological and chemical defense in military operations. Conventionally, these techniques preprocess the observed spectra using smoothing or baseline correction and apply detection algorithms like the generalized likelihood ratio test, independent component analysis, nonnegative matrix factorization, etc. These conventional detection algorithms need preprocessing and multiple shots of Raman spectra to get a reasonable accuracy. Recently, techniques based on deep learning are being used for target detection and classification due to its great adaptability and high accuracy over other methods and due to no requirement for preprocessing. Deep learning may give a good performance, but need retraining when untrained class targets are introduced which is time-consuming and bothersome. We devise a novel algorithm using a variant of the pseudo-Siamese network, one of the deep learning algorithms, that does not need retraining to detect and classify untrained class targets. Our algorithm detects and classifies targets with only one shot. In addition, our algorithm does not need preprocessing. We verify our algorithm with Raman spectra measured using a Raman spectrometer.Graphic AbstractWe devise our network based on a pseudo-Siamese deep neural network (DNN). Thanks to the pseudo-Siamese DNN structure, our network detects and classifies untrained chemicals with only one shot without preprocessing or retraining.

참고문헌 (26)

J. Raman Spectrosc. Gares 47 2016 10.1002/jrs.4868 124

상세보기
Raman spectroscopy for chemical analysis McCreery 225 2005 R. L.McCreery , Raman spectroscopy for chemical analysis , John Wiley & Sons , Columbus, Ohio , 2005 , vol. 225
Chemical and Biological Sensing III Higdon 2002 10.1117/12.472262 50 N. S.Higdon , T. H.Chyba , D. A.Richter , P. L.Ponsardin , W. T.Armstrong , C. T.Lobb , B. T.Kelly , R. D.Babnick and A. J.Sedlacek III , Chemical and Biological Sensing III , 2002 , pp. 50-59
Signal and Data Processing of Small Targets 2007 Slamani 2007 M.-A.Slamani , T. H.Chyba , H.LaValley and D.Emge , Signal and Data Processing of Small Targets 2007 , 2007 , p. 66991B
Signal and Data Processing of Small Targets 2008 Slamani 2008 10.1117/12.784560 696903 M.-A.Slamani , B.Fisk , T.Chyba , D.Emge and S.Waugh , Signal and Data Processing of Small Targets 2008 , 2008 , p. 696903
Curr. Opt. Photonics Ha 1 2017 247

원문보기 상세보기
Bull. Korean Chem. Soc. Choi 40 2019 10.1002/bkcs.11727 483

상세보기
Laser Source Technology for Defense and Security III Nagli 2007 L.Nagli and M.Gaft , Laser Source Technology for Defense and Security III , 2007 , p. 65520Z
Alvarez R.Alvarez , A. J.Burdette , X.Wu , C.Kotanen , Y.Zhao and R. A.Tripp
J. Signal Process. Syst. Wang 68 2012 10.1007/s11265-011-0625-7 379

상세보기
Fundamentals of statistical signal processing: Detection theory 1998 Fundamentals of statistical signal processing: Detection theory , ed. S. M. Kay , Prentice Hall PTR , New Jersey , 1998
Lincoln Lab. J. Manolakis 14 2003 79
Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XI Palkki 2010 10.1117/12.850644 766519 R. D.Palkki and A. D.Lanterman , Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XI , 2010 , p. 766519
Analyst Liu 142 2017 10.1039/C7AN01371J 4067

상세보기
MicrobiologyOpen Maruthamuthu 9 2020 10.1002/mbo3.1122 e1122

상세보기
Analyst Fan 144 2019 10.1039/C8AN02212G 1789

상세보기
Int. J. Remote Sens. Sun 41 2020 10.1080/01431161.2019.1667548 1349

상세보기
IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. Ye 12 2019 10.1109/JSTARS.2019.2951725 5047

상세보기
IEEE Trans. Ind. Electron. Wu 2021 1
IEEE Geosci. Remote Sens. Lett. Hughes 15 2018 10.1109/LGRS.2018.2799232 784

상세보기
ISPRS Int. J. Geo-Inf. Feng 8 2019 10.3390/ijgi8010028 28

상세보기
Bull. Korean Chem. Soc. Choi 40 2019 10.1002/bkcs.11679 279

상세보기
Application of Lidar to Current Atmospheric Topics III Ray 1999 10.1117/12.366437 50 M. D.Ray and A. J.Sedlacek III , Application of Lidar to Current Atmospheric Topics III , 1999 , pp. 50-59
Remote Sens. Environ. Huang 93 2004 10.1016/j.rse.2004.06.008 18

상세보기
Deep learning Goodfellow 1 2016 I.Goodfellow , Y.Bengio , A.Courville and Y.Bengio , Deep learning , MIT Press Cambridge , Cambridge, Massachusetts , 2016 , vol. 1
Anal. Methods He 6 2014 10.1039/C4AY00068D 4402

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DOI : 10.1039/D1AN01352A
Royal Society of Chemistry : 저널 > 논문

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