Multi-label classification with deep learning techniques applied to the B-Scan images of GPR
The ground penetrating radars (GPR) are now widely used for the detection of buried objects in areas such as: geology, archaeology and civil engineering. It has the advantage of allowing detection by a non-destructive technique. The principle for time domain GPR consists in emitting electromagnetic...
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Académie des sciences
2024-09-01
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| Series: | Comptes Rendus. Physique |
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| Online Access: | https://comptes-rendus.academie-sciences.fr/physique/articles/10.5802/crphys.193/ |
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| author | El Karakhi, Soukayna Reineix, Alain Guiffaut, Christophe |
| author_facet | El Karakhi, Soukayna Reineix, Alain Guiffaut, Christophe |
| author_sort | El Karakhi, Soukayna |
| collection | DOAJ |
| description | The ground penetrating radars (GPR) are now widely used for the detection of buried objects in areas such as: geology, archaeology and civil engineering. It has the advantage of allowing detection by a non-destructive technique. The principle for time domain GPR consists in emitting electromagnetic pulses in the ground, these one are then diffracted by the targets to be detected. A single GPR signal trace captured at a position of the radar is a 1D signal called Ascan. A set of Ascan radar waveforms captured at a certain number of different consecutive positions along a particular direction will form a 2D image called B-scan. They show response shapes of hyperbolic type and their analysis give many characteristics. For example, in the case of buried pipes, a specific processing allows to find their diameter, their nature as well as the electrical characteristics of the ground. However, these approaches often require complex post-processing of the Bscan, which can be time-consuming and therefore makes it difficult to perform real-time characterization at the expense of such methods. With the emergence of deep neural networks and with a learning phase on a large number of Bscan, it becomes possible to extract almost instantaneously the characteristics of GPR radar data. In this study, a multi-label classification (MLC) model based on transfer learning and data augmentation has been developed to generate multiple information elements on the same image and to realize classification. Three deep learning models: VGG-16, ResNet-50 and adapted CNN were used as pre-trained models for transfer learning. The networks were trained on a synthetic dataset created in this study and evaluated on a set of performance metrics. |
| format | Article |
| id | doaj-art-dd0e80d8cfa74e5d80985932caaa8f43 |
| institution | Kabale University |
| issn | 1878-1535 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Académie des sciences |
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| series | Comptes Rendus. Physique |
| spelling | doaj-art-dd0e80d8cfa74e5d80985932caaa8f432025-02-07T13:53:29ZengAcadémie des sciencesComptes Rendus. Physique1878-15352024-09-0125S110912410.5802/crphys.19310.5802/crphys.193Multi-label classification with deep learning techniques applied to the B-Scan images of GPREl Karakhi, Soukayna0Reineix, Alain1https://orcid.org/0000-0002-9439-6258Guiffaut, Christophe2https://orcid.org/0000-0002-7550-9966University of Limoges, XLIM Institute, 123 Av. Albert Thomas, 87000 Limoges, FranceUniversity of Limoges, XLIM Institute, 123 Av. Albert Thomas, 87000 Limoges, FranceUniversity of Limoges, XLIM Institute, 123 Av. Albert Thomas, 87000 Limoges, FranceThe ground penetrating radars (GPR) are now widely used for the detection of buried objects in areas such as: geology, archaeology and civil engineering. It has the advantage of allowing detection by a non-destructive technique. The principle for time domain GPR consists in emitting electromagnetic pulses in the ground, these one are then diffracted by the targets to be detected. A single GPR signal trace captured at a position of the radar is a 1D signal called Ascan. A set of Ascan radar waveforms captured at a certain number of different consecutive positions along a particular direction will form a 2D image called B-scan. They show response shapes of hyperbolic type and their analysis give many characteristics. For example, in the case of buried pipes, a specific processing allows to find their diameter, their nature as well as the electrical characteristics of the ground. However, these approaches often require complex post-processing of the Bscan, which can be time-consuming and therefore makes it difficult to perform real-time characterization at the expense of such methods. With the emergence of deep neural networks and with a learning phase on a large number of Bscan, it becomes possible to extract almost instantaneously the characteristics of GPR radar data. In this study, a multi-label classification (MLC) model based on transfer learning and data augmentation has been developed to generate multiple information elements on the same image and to realize classification. Three deep learning models: VGG-16, ResNet-50 and adapted CNN were used as pre-trained models for transfer learning. The networks were trained on a synthetic dataset created in this study and evaluated on a set of performance metrics.https://comptes-rendus.academie-sciences.fr/physique/articles/10.5802/crphys.193/Ground Penetrating RadarImage processingDetection of Buried objectsDeep learning |
| spellingShingle | El Karakhi, Soukayna Reineix, Alain Guiffaut, Christophe Multi-label classification with deep learning techniques applied to the B-Scan images of GPR Comptes Rendus. Physique Ground Penetrating Radar Image processing Detection of Buried objects Deep learning |
| title | Multi-label classification with deep learning techniques applied to the B-Scan images of GPR |
| title_full | Multi-label classification with deep learning techniques applied to the B-Scan images of GPR |
| title_fullStr | Multi-label classification with deep learning techniques applied to the B-Scan images of GPR |
| title_full_unstemmed | Multi-label classification with deep learning techniques applied to the B-Scan images of GPR |
| title_short | Multi-label classification with deep learning techniques applied to the B-Scan images of GPR |
| title_sort | multi label classification with deep learning techniques applied to the b scan images of gpr |
| topic | Ground Penetrating Radar Image processing Detection of Buried objects Deep learning |
| url | https://comptes-rendus.academie-sciences.fr/physique/articles/10.5802/crphys.193/ |
| work_keys_str_mv | AT elkarakhisoukayna multilabelclassificationwithdeeplearningtechniquesappliedtothebscanimagesofgpr AT reineixalain multilabelclassificationwithdeeplearningtechniquesappliedtothebscanimagesofgpr AT guiffautchristophe multilabelclassificationwithdeeplearningtechniquesappliedtothebscanimagesofgpr |