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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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 61; S. 1
Hauptverfasser: Cui, Guangyan, Xu, Jie, Wang, Yanhui, Zhao, Shengsheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Zusammenfassung: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%.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3228571