Multiple objects automatic detection of GPR data based on the AC-EWV and Genetic Algorithm

The automatic detection of multiple objects in Ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects, which can reduce the subjectivity of operators and improve the identification accuracy. Based on Frequency-wavenumber (F-K) migration, the a...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 61; s. 1
Hlavní autori:	Cui, Guangyan, Xu, Jie, Wang, Yanhui, Zhao, Shengsheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accurate Calculation of Electromagnetic Wave Velocity (AC-EWV) Algorithms Detection Electromagnetic radiation Electromagnetic scattering Entropy F-K migration Fitting Genetic algorithm Genetic algorithms GPR Ground penetrating radar Hyperbolas Machine learning algorithms Multiple objects automatic detection Object recognition Pipelines Radar Rebar Search problems Searching Voids Wave velocity Wavelengths
ISSN:	0196-2892, 1558-0644
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Abstract	The automatic detection of multiple objects in Ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects, which can reduce the subjectivity of operators and improve the identification accuracy. Based on Frequency-wavenumber (F-K) migration, the accurate calculation of electromagnetic wave velocity (AC-EWV) is proposed by searching for the minimum image entropy of migrated radargrams. To avoid global searching, potential positions of object hyperbolas are selected from the binarized radargram through the vertical gray gradient searching, then the sub_window is extracted with the potential position as the center. The best fitting hyperbola is detected with the genetic algorithm (GA) in the sub_window, and objects are finally determined with five hyperbolic matching criteria and the auto-categorization. This technique is verified with the simulated and measured GPR data about rebars, pipelines, and voids, and results demonstrate that it achieves the average correct rate, average missed rate, and the average misjudged rate is 98.46 %, 1.33%, and 0.36% respectively, and the average correct rate for GPR data of the double-layer rebars is 91.67%.
AbstractList	The automatic detection of multiple objects in Ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects, which can reduce the subjectivity of operators and improve the identification accuracy. Based on Frequency-wavenumber (F-K) migration, the accurate calculation of electromagnetic wave velocity (AC-EWV) is proposed by searching for the minimum image entropy of migrated radargrams. To avoid global searching, potential positions of object hyperbolas are selected from the binarized radargram through the vertical gray gradient searching, then the sub_window is extracted with the potential position as the center. The best fitting hyperbola is detected with the genetic algorithm (GA) in the sub_window, and objects are finally determined with five hyperbolic matching criteria and the auto-categorization. This technique is verified with the simulated and measured GPR data about rebars, pipelines, and voids, and results demonstrate that it achieves the average correct rate, average missed rate, and the average misjudged rate is 98.46 %, 1.33%, and 0.36% respectively, and the average correct rate for GPR data of the double-layer rebars is 91.67%. The automatic detection of multiple objects in ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects, which can reduce the subjectivity of operators and improve identification accuracy. Based on Frequency-wavenumber (F-K) migration, the accurate calculation of electromagnetic wave velocity (AC-EWV) is proposed by searching for the minimum image entropy of migrated radargrams. To avoid global searching, potential positions of object hyperbolas are selected from the binarized radargram through the vertical gray gradient searching, then the sub_window is extracted with the potential position as the center. The best fitting hyperbola is detected with the genetic algorithm (GA) in the sub_window, and objects are finally determined with five hyperbolic matching criteria and the auto-categorization. This technique is verified with the simulated and measured GPR data about rebars, pipelines, and voids, and results demonstrate that it achieves the average correct rate, average missed rate, and the average misjudged rate is 98.46%, 1.33%, and 0.36%, respectively, and the average correct rate for GPR data of the double-layer rebars is 91.67%.
Author	Cui, Guangyan Xu, Jie Zhao, Shengsheng Wang, Yanhui
Author_xml	– sequence: 1 givenname: Guangyan orcidid: 0000-0003-2251-2616 surname: Cui fullname: Cui, Guangyan organization: School of Traffic and Transportation, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China – sequence: 2 givenname: Jie surname: Xu fullname: Xu, Jie organization: School of Traffic and Transportation, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China – sequence: 3 givenname: Yanhui orcidid: 0000-0002-2405-7306 surname: Wang fullname: Wang, Yanhui organization: School of Traffic and Transportation, Beijing Research Center of Urban Traffic Information Sensing and Service Technology, Research and Development Center of Transport Industry of Technologies and Equipment of Urban Rail Operation Safety Management, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China – sequence: 4 givenname: Shengsheng surname: Zhao fullname: Zhao, Shengsheng organization: School of Traffic and Transportation, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
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SubjectTerms	Accurate Calculation of Electromagnetic Wave Velocity (AC-EWV) Algorithms Detection Electromagnetic radiation Electromagnetic scattering Entropy F-K migration Fitting Genetic algorithm Genetic algorithms GPR Ground penetrating radar Hyperbolas Machine learning algorithms Multiple objects automatic detection Object recognition Pipelines Radar Rebar Search problems Searching Voids Wave velocity Wavelengths
Title	Multiple objects automatic detection of GPR data based on the AC-EWV and Genetic Algorithm
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