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Buried Object Detection from B-Scan Ground Penetrating Radar Data Using Faster-RCNN

Minh-Tan Pham 1 Sébastien Lefèvre 1
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this paper, we adapt the Faster-RCNN framework for the detection of underground buried objects (i.e. hyperbola reflections) in B-scan ground penetrating radar (GPR) images. Due to the lack of real data for training, we propose to incorporate more simulated radargrams generated from different configurations using the gprMax toolbox. Our designed CNN is first pre-trained on the grayscale Cifar-10 database. Then, the Faster-RCNN framework based on the pre-trained CNN is trained and fine-tuned on both real and simulated GPR data. Preliminary detection results show that the proposed technique can provide significant improvements compared to classical computer vision methods and hence becomes quite promising to deal with this kind of specific GPR data even with few training samples.
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Submitted on : Wednesday, November 13, 2019 - 6:36:10 PM
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Minh-Tan Pham, Sébastien Lefèvre. Buried Object Detection from B-Scan Ground Penetrating Radar Data Using Faster-RCNN. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, Valencia, Spain. ⟨10.1109/IGARSS.2018.8517683⟩. ⟨hal-01969029⟩

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