[Classification multi-label par des techniques d’apprentissage profond appliquées aux images B-Scan de radars à sondage de sol]
Les radars à pénétration de sol (GPR) sont aujourd’hui largement utilisés pour la détection d’objets enterrés dans des domaines tels que la géologie, l’archéologie et le génie civil. Il présente l’avantage de permettre une détection par une technique non destructive. Le principe du GPR dans le domaine temporel consiste à émettre des impulsions électromagnétiques dans le sol, celles-ci étant ensuite diffractées par les cibles à détecter. Une seule trace de signal GPR capturée à une position du radar est un signal 1D appelé Ascan. Un ensemble de formes d’ondes radar Ascan capturées à un certain nombre de positions consécutives différentes le long d’une direction particulière formera une image 2D appelée B-scan dans le cas d’un déplacement rectiligne. Elles montrent des formes de réponse de type hyperbolique et leur analyse donne de nombreuses caractéristiques. Par exemple, dans le cas de canalisations enterrées, un traitement spécifique permet de connaître leur diamètre, leur nature ainsi que les caractéristiques électriques du sol. Cependant, ces approches nécessitent souvent un post-traitement complexe du Bscan, ce qui peut être chronophage et rend donc difficile la caractérisation en temps réel au détriment de ces méthodes. Avec l’émergence des réseaux neuronaux profonds et avec une phase d’apprentissage sur un grand nombre de Bscan, il devient possible d’extraire presque instantanément les caractéristiques des données radar GPR. Dans cette étude, un modèle de classification multi-label (MLC) basé sur l’apprentissage par transfert et l’augmentation des données a été développé pour générer des éléments d’information multiples sur la même image et réaliser la classification. Trois modèles d’apprentissage profond : VGG-16, ResNet-50 et CNN adapté ont été utilisés comme modèles pré-entraînés pour l’apprentissage par transfert. Les réseaux ont été formés sur un ensemble de données synthétiques créé dans cette étude et évalués sur un ensemble de mesures de performance.
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.
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Mot clés : Radar à sondage de sol, traitement d’images, détection d’objets enfouis, apprentissage profond
Soukayna El Karakhi 1 ; Alain Reineix 1 ; Christophe Guiffaut 1
CC-BY 4.0
@article{CRPHYS_2024__25_S1_A12_0,
author = {Soukayna El Karakhi and Alain Reineix and Christophe Guiffaut},
title = {Multi-label classification with deep learning techniques applied to the {B-Scan} images of {GPR}},
journal = {Comptes Rendus. Physique},
publisher = {Acad\'emie des sciences, Paris},
year = {2024},
doi = {10.5802/crphys.193},
language = {en},
note = {Online first},
}
TY - JOUR AU - Soukayna El Karakhi AU - Alain Reineix AU - Christophe Guiffaut TI - Multi-label classification with deep learning techniques applied to the B-Scan images of GPR JO - Comptes Rendus. Physique PY - 2024 PB - Académie des sciences, Paris N1 - Online first DO - 10.5802/crphys.193 LA - en ID - CRPHYS_2024__25_S1_A12_0 ER -
%0 Journal Article %A Soukayna El Karakhi %A Alain Reineix %A Christophe Guiffaut %T Multi-label classification with deep learning techniques applied to the B-Scan images of GPR %J Comptes Rendus. Physique %D 2024 %I Académie des sciences, Paris %Z Online first %R 10.5802/crphys.193 %G en %F CRPHYS_2024__25_S1_A12_0
Soukayna El Karakhi; Alain Reineix; Christophe Guiffaut. Multi-label classification with deep learning techniques applied to the B-Scan images of GPR. Comptes Rendus. Physique, Online first (2024), pp. 1-16. doi : 10.5802/crphys.193.
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